Next Issue
Volume 15, June-2
Previous Issue
Volume 15, May-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Volume 15
Issue 11
remotesensing-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sens., Volume 15, Issue 11 (June-1 2023) – 252 articles

Cover Story (view full-size image): Wildfires are a major disaster, annually burning millions of acres in the US alone and their accurate detection is crucial for mitigation. Uncertainty quantification is vital, as it provides more insight for decision-making. This paper proposes a supervised deep generative machine learning model for wildfire detection and projection. The model generates plausible segmentations representing expert disagreements. The model combines latent distributions and visual features, forming a supervised stochastic image-to-image detection model. Experiments suggest better agreement between the proposed model and ground-truth segmentations. It exhibits superior comprehension of wildfire dynamics. Our model enables accurate detection and uncertainty quantification, aiding authorities, and researchers in combating wildfires. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
38 pages, 14304 KiB  
Article
Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation
by Yunfeng Zhang, Yiming Wang, Yonggang Ji and Ming Li
Remote Sens. 2023, 15(11), 2952; https://doi.org/10.3390/rs15112952 - 05 Jun 2023
Viewed by 1143
Abstract
Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, [...] Read more.
Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT). Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)
►▼ Show Figures

Graphical abstract

29 pages, 12696 KiB  
Article
Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes
by Haiyang Zhang, Yao Zhang, Tingyao Gao, Shu Lan, Fanghui Tong and Minzan Li
Remote Sens. 2023, 15(11), 2951; https://doi.org/10.3390/rs15112951 - 05 Jun 2023
Cited by 1 | Viewed by 1942
Abstract
Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, [...] Read more.
Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring. Full article
(This article belongs to the Special Issue Digital Farming with Remote Sensing)
►▼ Show Figures

Figure 1

36 pages, 7870 KiB  
Article
Trade-Off and Synergy Relationships and Spatial Bundle Analysis of Ecosystem Services in the Qilian Mountains
by Yipeng Wang, Hongyi Cheng, Naiang Wang, Chufang Huang, Kaili Zhang, Bin Qiao, Yuanyuan Wang and Penghui Wen
Remote Sens. 2023, 15(11), 2950; https://doi.org/10.3390/rs15112950 - 05 Jun 2023
Cited by 4 | Viewed by 1267
Abstract
Significant heterogeneity has been observed among different ecosystem services (ES). Understanding the trade-offs and synergies among ES and delineating ecological functional zones is crucial for formulating regional management policies that improve human well-being and sustainably develop and maintain ecosystems. In this study, we [...] Read more.
Significant heterogeneity has been observed among different ecosystem services (ES). Understanding the trade-offs and synergies among ES and delineating ecological functional zones is crucial for formulating regional management policies that improve human well-being and sustainably develop and maintain ecosystems. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Carnegie–Ames–Stanford Approach (CASA) models to evaluate the spatial distribution patterns of nine ES (food supply, raw material supply, water resource supply, water connotation, climate regulation, soil conservation, water purification, habitat quality, and entertainment tourism) in the Qilian Mountains from 2000 to 2018. We also investigated the trade-offs and synergistic relationships among ES through Spearman correlation analysis, identified ES hotspots through exploratory spatial data analysis, and identified ES bundles (ESB) using K-means clustering. Our results revealed that water purification and habitat quality remained relatively stable, while food supply, raw material supply, water resource supply, water conservation, climate regulation, soil conservation, and entertainment tourism increased by 1038.83 Yuan·ha−1, 448.21 Yuan·ha−1, 55.45 mm, 7.80 mm, 0.60 tc·ha−1, 40.01 t·ha−1 and 4.82, respectively. High-value areas for water resource supply were mainly concentrated in the high-altitude mountainous area, whereas high-value areas for soil conservation were found in the western and eastern parts of the study area. The low-value areas of water purification were primarily located in the east, while the remaining six services were highly distributed in the east and were less common in the west. Correlation analysis showed that water resource supply, water conservation, and soil conservation exhibited a synergistic relationship in the Qilian Mountains. Moreover, food supply, raw material supply, climate regulation, habitat quality, and entertainment tourism showed synergistic relationships. However, there were trade-offs between food supply and water purification as well as water resource supply, and habitat quality showed a tradeoff with water resource supply, water conservation, and soil conservation. We identified four ESB. The food supply bundle consisted mainly of farmland ecosystems, while the windbreak and sand fixation and ecological coordination bundles were dominant in the Qilian Mountains. Notably, the area of the water conservation bundle increased significantly. Our comprehensive findings on ES and ESB can provide a theoretical foundation for the formulation of ecological management policies and the sustainable development of ecosystems in the Qilian Mountains. Full article
(This article belongs to the Special Issue Remote Sensing Based Monitoring of Terrestrial Ecosystem Service Bundles, Trade-Offs and Synergies (Second Edition))
►▼ Show Figures

Figure 1

19 pages, 6623 KiB  
Article
A Bio-Inspired MEMS Wake Detector for AUV Tracking and Coordinated Formation
by Qingyu Qiao, Xiangzheng Kong, Shufeng Wu, Guochang Liu, Guojun Zhang, Hua Yang, Wendong Zhang, Yuhua Yang, Licheng Jia, Changde He, Jiangong Cui and Renxin Wang
Remote Sens. 2023, 15(11), 2949; https://doi.org/10.3390/rs15112949 - 05 Jun 2023
Cited by 1 | Viewed by 1161
Abstract
AUV (Autonomous Underwater Vehicle) coordinated formation can expand the detection range, improve detection efficiency, and complete complex tasks, which requires each AUV to have the ability to track and locate. A wake detector provides a new technical approach for AUV cooperative formation warfare. [...] Read more.
AUV (Autonomous Underwater Vehicle) coordinated formation can expand the detection range, improve detection efficiency, and complete complex tasks, which requires each AUV to have the ability to track and locate. A wake detector provides a new technical approach for AUV cooperative formation warfare. Now, most of the existing artificial lateral line detectors are for one-dimensional flow field applications, which are difficult to use for wake detection of AUVs. Therefore, based on the pressure gradient sensing mechanism of the canal neuromasts, we apply Micro-Electro-Mechanical System (MEMS) technology to develop a lateral line-inspired MEMS wake detector. The sensing mechanism, design, and fabrication are demonstrated in detail. Experimental results show the detector’s sensitivity is 147 mV·(m/s)−1, and the detection threshold is 0.3 m/s. In addition, the vector test results verify it has vector-detecting capacity. This wake detector can serve AUVs wake detection and tracking technology, which will be promising in AUV positioning and coordinated formation. Full article
(This article belongs to the Special Issue Advances on Autonomous Underwater Vehicles (AUV))
►▼ Show Figures

Figure 1

24 pages, 6832 KiB  
Article
Developing Spatial and Temporal Continuous Fractional Vegetation Cover Based on Landsat and Sentinel-2 Data with a Deep Learning Approach
by Zihao Wang, Dan-Xia Song, Tao He, Jun Lu, Caiqun Wang and Dantong Zhong
Remote Sens. 2023, 15(11), 2948; https://doi.org/10.3390/rs15112948 - 05 Jun 2023
Cited by 4 | Viewed by 1627
Abstract
Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites [...] Read more.
Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R2 = 0.857), followed by the data-fusion method (R2 = 0.709) and curve-fitting method (R2 = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation: Mapping, Trend Analysis, and Drivers of Change)
►▼ Show Figures

Figure 1

46 pages, 57708 KiB  
Article
Impacts of Climate Change and Human Activities on Plant Species α-Diversity across the Tibetan Grasslands
by Shaolin Huang and Gang Fu
Remote Sens. 2023, 15(11), 2947; https://doi.org/10.3390/rs15112947 - 05 Jun 2023
Cited by 10 | Viewed by 1806
Abstract
Plant species α-diversity is closely correlated with ecosystem structures and functions. However, whether climate change and human activities will reduce plant species α-diversity remains controversial. In this study, potential (i.e., potential species richness: SRp, Shannonp, Simpsonp and Pieloup) and actual plant species α-diversity [...] Read more.
Plant species α-diversity is closely correlated with ecosystem structures and functions. However, whether climate change and human activities will reduce plant species α-diversity remains controversial. In this study, potential (i.e., potential species richness: SRp, Shannonp, Simpsonp and Pieloup) and actual plant species α-diversity (i.e., actual species richness: SRa, Shannona, Simpsona and Pieloua) during 2000–2020 were quantified based on random forests in grasslands on the Tibetan Plateau. Overall, climate change had positive influences on potential plant species α-diversity across all the grassland systems. However, more than one-third areas showed decreasing trends for potential plant species α-diversity. Climate change increased the SRp at rates of 0.0060 and 0.0025 yr−1 in alpine steppes and alpine meadows, respectively. Temperature change predominated the variations of Shannonp and Simpsonp, and radiation change predominated the variations of SRp and Pieloup. Geography position, local temperature, precipitation and radiation conditions regulated the impacts of climate change on potential species α-diversity. On average, human activities caused 1% plant species loss but elevated the Shannon, Simpson and Pielou by 26%, 4% and 5%, respectively. There were 46.51%, 81.08%, 61.26% and 61.10% areas showing positive effects of human activities on plant species richness, Shannon, Simpson and Pielou, respectively. There were less than 48% areas showing increasing trends of human activities’ impacts on plant species α-diversity. Human activities increased plant species richness by 2% in alpine meadows but decreased plant species richness by 1% in alpine steppes. Accordingly, both the impacts of climate change and human activities on plant species α-diversity were not always negative and varied with space and grassland types. The study warned that both climate change and human activities may not cause as much species loss as expected. This study also cautioned that the impacts of radiation change on plant species α-diversity should be at least put on the same level as the impacts of climate warming and precipitation change on plant α-diversity. Full article
►▼ Show Figures

Figure 1

26 pages, 20017 KiB  
Article
An Extraction Method for Large Gradient Three-Dimensional Displacements of Mining Areas Using Single-Track InSAR, Boltzmann Function, and Subsidence Characteristics
by Kegui Jiang, Keming Yang, Yanhai Zhang, Yaxing Li, Tingting Li and Xiangtong Zhao
Remote Sens. 2023, 15(11), 2946; https://doi.org/10.3390/rs15112946 - 05 Jun 2023
Cited by 2 | Viewed by 1003
Abstract
This paper presents an extraction method for large gradient three-dimensional (3-D) displacements of mining areas using single-track interferometric synthetic aperture radar (InSAR), Boltzmann function, and subsidence characteristics. This is mainly aimed at overcoming the limitations of surface deformation monitoring in mining areas by [...] Read more.
This paper presents an extraction method for large gradient three-dimensional (3-D) displacements of mining areas using single-track interferometric synthetic aperture radar (InSAR), Boltzmann function, and subsidence characteristics. This is mainly aimed at overcoming the limitations of surface deformation monitoring in mining areas by using single-track InSAR technology. One is that the rapid and large gradient deformation of the mine surface usually leads to image decoherence, which makes it difficult to obtain correct deformation information. Second, the surface deformation monitored by InSAR is only one-dimensional line of sight (LOS) displacement, and thus it is difficult to reflect the real 3-D displacements of the surface. Firstly, the Boltzmann function prediction model (BPM) is introduced to assist InSAR phase unwrapping; thus the missing large gradient deformation phase of InSAR is recovered. Then, the subsidence characteristics in mining horizontal (or near-horizontal) coal seams are used as prior knowledge for theoretical derivation, and a 3-D displacement extraction model of coal seam mining with single-track InSAR is constructed. The feasibility of the method is verified by simulating LOS displacements with random noise and underestimation phenomenon caused by the large gradient deformation as InSAR observations. The results show that the root mean square error (RMSE) of 3-D displacements on the observation line calculated by the proposed method is 21.5 mm, 19.0 mm, and 32.9 mm, respectively. Based on the single-track Sentinel-1 images, the method in this paper was applied to the extraction of surface 3-D displacements in the Huaibei coal mine, and the experimental results show that the extracted 3-D displacements are in good agreement with that of measurement by the surface observation station. The proposed method can adapt to limited InSAR acquisitions and complex monitoring environments. Full article
(This article belongs to the Section AI Remote Sensing)
►▼ Show Figures

Graphical abstract

20 pages, 106782 KiB  
Article
Improved Generalized IHS Based on Total Variation for Pansharpening
by Xuefeng Zhang, Xiaobing Dai, Xuemin Zhang, Yuchen Hu, Yingdong Kang and Guang Jin
Remote Sens. 2023, 15(11), 2945; https://doi.org/10.3390/rs15112945 - 05 Jun 2023
Cited by 2 | Viewed by 1141
Abstract
Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the [...] Read more.
Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the spatial and spectral fidelity in fused images. The spectral distortion is widespread in the component substitution-based approaches due to the variation in the intensity distribution of spatial components. We lightened the idea using the total variation optimization to improve upon a novel GIHS-TV framework for pansharpening. The framework drew the high spatial fidelity from the GIHS scheme and implemented it with a simpler variational expression. An improved L1-TV constraint to the new spatial–spectral information was introduced to the GIHS-TV framework, along with its fast implementation. The objective function was solved by the Iteratively Reweighted Norm (IRN) method. The experimental results on the “PAirMax” dataset clearly indicated that GIHS-TV could effectively reduce the spectral distortion in the process of component substitution. Our method has achieved excellent results in visual effects and evaluation metrics. Full article
(This article belongs to the Special Issue Machine Vision and Advanced Image Processing in Remote Sensing II)
►▼ Show Figures

Graphical abstract

34 pages, 62588 KiB  
Technical Note
Use of ICEsat-2 and Sentinel-2 Open Data for the Derivation of Bathymetry in Shallow Waters: Case Studies in Sardinia and in the Venice Lagoon
by Massimo Bernardis, Roberto Nardini, Lorenza Apicella, Maurizio Demarte, Matteo Guideri, Bianca Federici, Alfonso Quarati and Monica De Martino
Remote Sens. 2023, 15(11), 2944; https://doi.org/10.3390/rs15112944 - 05 Jun 2023
Cited by 1 | Viewed by 1972
Abstract
Despite the high accuracy of conventional acoustic hydrographic systems, measurement of the seabed along coastal belts is still a complex problem due to the limitations arising from shallow water. In addition to traditional echo sounders, airborne LiDAR also suffers from high application costs, [...] Read more.
Despite the high accuracy of conventional acoustic hydrographic systems, measurement of the seabed along coastal belts is still a complex problem due to the limitations arising from shallow water. In addition to traditional echo sounders, airborne LiDAR also suffers from high application costs, low efficiency, and limited coverage. On the other hand, remote sensing offers a practical alternative for the extraction of depth information, providing fast, reproducible, low-cost mapping over large areas to optimize and minimize fieldwork. Satellite-derived bathymetry (SDB) techniques have proven to be a promising alternative to supply shallow-water bathymetry data. However, this methodology is still limited since it usually requires in situ observations as control points for multispectral imagery calibration and bathymetric validation. In this context, this paper illustrates the potential for bathymetric derivation conducted entirely from open satellite data, without relying on in situ data collected using traditional methods. The SDB was performed using multispectral images from Sentinel-2 and bathymetric data collected by NASA’s ICESat-2 on two areas of relevant interest. To assess outcomes’ reliability, bathymetries extracted from ICESat-2 and derived from Sentinel-2 were compared with the updated and reliable data from the BathyDataBase of the Italian Hydrographic Institute. Full article
(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)
►▼ Show Figures

Graphical abstract

18 pages, 18394 KiB  
Article
Predictive Mapping of Mediterranean Seagrasses-Exploring the Influence of Seafloor Light and Wave Energy on Their Fine-Scale Spatial Variability
by Elias Fakiris, Vasileios Giannakopoulos, Georgios Leftheriotis, Athanassios Dimas and George Papatheodorou
Remote Sens. 2023, 15(11), 2943; https://doi.org/10.3390/rs15112943 - 05 Jun 2023
Viewed by 2145
Abstract
Seagrasses are flowering plants, adapted to marine environments, that are highly diverse in the Mediterranean Sea and provide a variety of ecosystem services. It is commonly recognized that light availability sets the lower limit of seagrass bathymetric distribution, while the upper limit depends [...] Read more.
Seagrasses are flowering plants, adapted to marine environments, that are highly diverse in the Mediterranean Sea and provide a variety of ecosystem services. It is commonly recognized that light availability sets the lower limit of seagrass bathymetric distribution, while the upper limit depends on the level of bottom disturbance by currents and waves. In this work, detailed distribution of seagrass, obtained through geoacoustic habitat mapping and optical ground truthing, is correlated to wave energy and light on the seafloor of the Marine Protected Area of Laganas Bay, Zakynthos Island, Greece, where the seagrasses Posidonia oceanica and Cymodocea nodosa form extensive meadows. Mean wave energy on the seafloor was estimated through wave propagation modeling, while the photosynthetically active radiation through open-access satellite-derived light parameters, reduced to the seafloor using the detailed acquired bathymetry. A significant correlation of seagrass distribution with wave energy and light was made clear, allowing for performing fine-scale predictive seagrass mapping using a random forest classifier. The predicted distributions exhibited >80% overall accuracy for P. oceanica and >90% for C. nodosa, indicating that fine-scale seagrass predictive mapping in the Mediterranean can be performed robustly through bottom wave energy and light, especially when detailed bathymetric data exist to allow for accurate estimations. Full article
(This article belongs to the Special Issue Remote Sensing for Coastal Habitat Mapping and Decade of Ocean Science)
►▼ Show Figures

Figure 1

22 pages, 8069 KiB  
Article
Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning
by Yingkang Huang, Xiaorong Wen, Yuanyun Gao, Yanli Zhang and Guozhong Lin
Remote Sens. 2023, 15(11), 2942; https://doi.org/10.3390/rs15112942 - 05 Jun 2023
Cited by 3 | Viewed by 2148
Abstract
We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as [...] Read more.
We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images. Full article
(This article belongs to the Topic Deep Learning and Transformers’ Methods Applied to Remotely Captured Data)
►▼ Show Figures

Graphical abstract

44 pages, 7087 KiB  
Review
Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review
by Ali Fares and Tarek Zayed
Remote Sens. 2023, 15(11), 2941; https://doi.org/10.3390/rs15112941 - 05 Jun 2023
Cited by 5 | Viewed by 1504
Abstract
Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated [...] Read more.
Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry. Full article
(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)
►▼ Show Figures

Figure 1

14 pages, 4444 KiB  
Technical Note
Assessing Transferability of Remote Sensing Pasture Estimates Using Multiple Machine Learning Algorithms and Evaluation Structures
by Hunter D. Smith, Jose C. B. Dubeux, Alina Zare and Chris H. Wilson
Remote Sens. 2023, 15(11), 2940; https://doi.org/10.3390/rs15112940 - 05 Jun 2023
Cited by 2 | Viewed by 1170
Abstract
Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly [...] Read more.
Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly train and test on site-specific or homogenized—i.e., randomly partitioned—datasets and choose complex ML algorithms with increased potential to overfit a limited dataset. In this study, we evaluated the accuracy and transferability of remote sensing pasture models, using multiple ML algorithms and evaluation structures. Specifically, we predicted pasture above-ground biomass and nitrogen concentration from Sentinel-2 imagery. The implemented ML algorithms include principal components regression (PCR), partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine regression (SVR), and a gradient boosting model (GBM). The evaluation structures were determined using levels of spatial and temporal dissimilarity to partition the train and test datasets. Our results demonstrated a general decline in accuracy as evaluation structures increase in spatiotemporal dissimilarity. In addition, the more simplistic algorithms—PCR, PLSR, and LASSO—out-performed the more complex models RF, SVR, and GBM for the prediction of dissimilar evaluation structures. We conclude that multi-spectral satellite and pasture physiological variable datasets, such as the one presented in this study, contain spatiotemporal internal dependence, which makes the generalization of predictive models to new localities challenging, especially for complex ML algorithms. Further studies on this topic should include the assessment of model transferability by using dissimilar evaluation structures, and we expect generalization to improve for larger and denser datasets. Full article
►▼ Show Figures

Graphical abstract

18 pages, 2512 KiB  
Article
An Extended Simultaneous Algebraic Reconstruction Technique for Imaging the Ionosphere Using GNSS Data and Its Preliminary Results
by Yuanliang Long, Xingliang Huo, Haojie Liu, Ying Li and Weihong Sun
Remote Sens. 2023, 15(11), 2939; https://doi.org/10.3390/rs15112939 - 05 Jun 2023
Viewed by 1114
Abstract
To generate high-quality reconstructions of ionospheric electron density (IED), we propose an extended simultaneous algebraic reconstruction technique (ESART). The ESART method distributes the discrepancy between the actual GNSS TEC and the calculated TEC among the ray–voxels based on the contribution of voxels to [...] Read more.
To generate high-quality reconstructions of ionospheric electron density (IED), we propose an extended simultaneous algebraic reconstruction technique (ESART). The ESART method distributes the discrepancy between the actual GNSS TEC and the calculated TEC among the ray–voxels based on the contribution of voxels to GNSS TEC, rather than the ratio of the length of ray–voxel intersection to the sum of the lengths of all ray–voxel intersections, as is adopted by conventional methods. The feasibility of the ESART method for reconstructing the IED under different levels of geomagnetic activities is addressed. Additionally, a preliminary experiment is performed using the reconstructed IED profiles and comparing them with ionosonde measurements, which provide direct observations of electron density. The root mean square errors (RMSE) and absolute errors of the ESART method, the simultaneous algebraic reconstruction technique (SART) method, and the International Reference Ionosphere (IRI) 2016 model are calculated to evaluate the effectiveness of the proposed method. Compared to the conventional SART method of ionospheric tomography and the IRI-2016 model, the reconstructed IED profiles obtained using the ESART method are in better agreement with the electron density obtained from the ionosondes, especially for the peak electron densities (NmF2). In addition, a case study of an intense geomagnetic storm on 17–19 March 2015 shows that the spatial and temporal features of storm-related ionospheric disturbances can be more clearly depicted using the ESART method than with the SART method. Full article
(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)
►▼ Show Figures

Figure 1

29 pages, 23352 KiB  
Article
GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing
by Iman Esfandiyar, Krzysztof Ćwian, Michał R. Nowicki and Piotr Skrzypczyński
Remote Sens. 2023, 15(11), 2938; https://doi.org/10.3390/rs15112938 - 05 Jun 2023
Viewed by 1263
Abstract
Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find [...] Read more.
Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
►▼ Show Figures

Figure 1

18 pages, 10447 KiB  
Article
Deriving Agricultural Field Boundaries for Crop Management from Satellite Images Using Semantic Feature Pyramid Network
by Yang Xu, Xinyu Xue, Zhu Sun, Wei Gu, Longfei Cui, Yongkui Jin and Yubin Lan
Remote Sens. 2023, 15(11), 2937; https://doi.org/10.3390/rs15112937 - 05 Jun 2023
Cited by 1 | Viewed by 1737
Abstract
We propose a Semantic Feature Pyramid Network (FPN)-based algorithm to derive agricultural field boundaries and internal non-planting regions from satellite imagery. It is aimed at providing guidance not only for land use management, but more importantly for harvest or crop protection machinery planning. [...] Read more.
We propose a Semantic Feature Pyramid Network (FPN)-based algorithm to derive agricultural field boundaries and internal non-planting regions from satellite imagery. It is aimed at providing guidance not only for land use management, but more importantly for harvest or crop protection machinery planning. The Semantic Convolutional Neural Network (CNN) FPN is first employed for pixel-wise classification on each remote sensing image, detecting agricultural parcels; a post-processing method is then developed to transfer attained pixel classification results into closed contours, as field boundaries and internal non-planting regions, including slender paths (walking or water) and obstacles (trees or electronic poles). Three study sites with different plot sizes (0.11 ha, 1.39 ha, and 2.24 ha) are selected to validate the effectiveness of our algorithm, and the performance compared with other semantic CNN (including U-Net, U-Net++, PSP-Net, and Link-Net)-based algorithms. The test results show that the crop acreage information, field boundaries, and internal non-planting area could be determined by using the proposed algorithm in different places. When the boundary number applicable for machinery planning is attained, average and total crop planting area values all remain closer to the reference ones generally when using the semantic FPN with post-processing, compared with other methods. The post-processing methodology would greatly decrease the number of inapplicable and redundant field boundaries for path planning using different CNN models. In addition, the crop planting mode and scale (especially the small-scale planting and small/blurred gap between fields) both make a great difference to the boundary delineation and crop acreage determination. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
►▼ Show Figures

Graphical abstract

21 pages, 5382 KiB  
Article
MCPT: Mixed Convolutional Parallel Transformer for Polarimetric SAR Image Classification
by Wenke Wang, Jianlong Wang, Bibo Lu, Boyuan Liu, Yake Zhang and Chunyang Wang
Remote Sens. 2023, 15(11), 2936; https://doi.org/10.3390/rs15112936 - 05 Jun 2023
Cited by 1 | Viewed by 1293
Abstract
Vision transformers (ViT) have the characteristics of massive training data and complex model, which cannot be directly applied to polarimetric synthetic aperture radar (PolSAR) image classification tasks. Therefore, a mixed convolutional parallel transformer (MCPT) model based on ViT is proposed for fast PolSAR [...] Read more.
Vision transformers (ViT) have the characteristics of massive training data and complex model, which cannot be directly applied to polarimetric synthetic aperture radar (PolSAR) image classification tasks. Therefore, a mixed convolutional parallel transformer (MCPT) model based on ViT is proposed for fast PolSAR image classification. First of all, a mixed depthwise convolution tokenization is introduced. It replaces the learnable linear projection in the original ViT to obtain patch embeddings. The process of tokenization can reduce computational and parameter complexity and extract features of different receptive fields as input to the encoder. Furthermore, combining the idea of shallow networks with lower latency and easier optimization, a parallel encoder is implemented by pairing the same modules and recombining to form parallel blocks, which can decrease the network depth and computing power requirement. In addition, the original class embedding and position embedding are removed during tokenization, and a global average pooling layer is added after the encoder for category feature extraction. Finally, the experimental results on AIRSAR Flevoland and RADARSAT-2 San Francisco datasets show that the proposed method achieves a significant improvement in training and prediction speed. Meanwhile, the overall accuracy achieved was 97.9% and 96.77%, respectively. Full article
(This article belongs to the Topic Deep Learning and Transformers’ Methods Applied to Remotely Captured Data)
►▼ Show Figures

Graphical abstract

19 pages, 7923 KiB  
Article
Research and Evaluation on Dynamic Maintenance of an Elevation Datum Based on CORS Network Deformation
by Shenghao Liang, Chuanyin Zhang, Tao Jiang and Wei Wang
Remote Sens. 2023, 15(11), 2935; https://doi.org/10.3390/rs15112935 - 05 Jun 2023
Viewed by 1100
Abstract
This paper presents a method for dynamically maintaining a regional elevation datum using CORS stations as core nodes. By utilizing CORS station data and surface mass loading data (including land water storage, sea level, and atmospheric pressure), the normal height changes of each [...] Read more.
This paper presents a method for dynamically maintaining a regional elevation datum using CORS stations as core nodes. By utilizing CORS station data and surface mass loading data (including land water storage, sea level, and atmospheric pressure), the normal height changes of each station can be determined and dynamically maintained. The validity of this method is verified using multiple leveling survey results from five CORS stations in Beijing’s subsidence area between January 2012 and June 2021. Results show that it is necessary to derive and correct the height anomaly variation of CORS stations caused by surface mass loading using the remove-calculate-restore method and the Green’s function integration method, with the influence of surface mass changes reaching a subcentimeter level. CORS stations exhibiting great observation quality achieve a mean accuracy of 2.7 mm in determining normal height changes. Such accuracy surpasses the requirements of second-class leveling surveys covering route lengths exceeding 1.35 km, as well as conforming/closed loop routes with distances greater than 0.46 km. By strategically selecting CORS stations with long-term continuous observations and high-quality data as core nodes within the elevation control network, dynamic maintenance of the regional elevation datum can be achieved based on CORS station data. Full article
(This article belongs to the Topic Development of Monitoring, Analysis and Maintenance Technics of Infrastructures)
►▼ Show Figures

Figure 1

21 pages, 52577 KiB  
Article
Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil
by Harold E. Pineda Valles, Gustavo Manzon Nunes, Christian Niel Berlinck, Luiz Gustavo Gonçalves and Gabriel Henrique Pires de Mello Ribeiro
Remote Sens. 2023, 15(11), 2934; https://doi.org/10.3390/rs15112934 - 04 Jun 2023
Viewed by 1552
Abstract
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, [...] Read more.
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making. Full article
(This article belongs to the Special Issue Structure and Trend Monitoring of Forest Vegetation and Savanna Based on UAS Platform)
►▼ Show Figures

Figure 1

25 pages, 5643 KiB  
Article
Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network
by Chaoyang Shi, Wenxin Teng, Yi Zhang, Yue Yu, Liang Chen, Ruizhi Chen and Qingquan Li
Remote Sens. 2023, 15(11), 2933; https://doi.org/10.3390/rs15112933 - 04 Jun 2023
Cited by 1 | Viewed by 1499
Abstract
Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of [...] Read more.
Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database. Full article
(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)
►▼ Show Figures

Figure 1

36 pages, 7030 KiB  
Review
Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture
by Sashini Pathirana, Sébastien Lambot, Manokarajah Krishnapillai, Mumtaz Cheema, Christina Smeaton and Lakshman Galagedara
Remote Sens. 2023, 15(11), 2932; https://doi.org/10.3390/rs15112932 - 04 Jun 2023
Cited by 5 | Viewed by 3537
Abstract
Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their [...] Read more.
Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their spatiotemporal variability. This paper reviews the fundamental operating principles of GPR and EMI, their applications in soil studies, advantages and disadvantages, and knowledge gaps leading to the identification of the difficulties in integrating these two techniques to complement each other in soil data studies. Compared to the traditional methods, GPR and EMI have advantages, such as the ability to take non-destructive repeated measurements, high resolution, being labor-saving, and having more extensive spatial coverage with geo-referenced data within agricultural landscapes. GPR has been widely used to estimate soil water content (SWC) and water dynamics, while EMI has broader applications such as estimating SWC, soil salinity, bulk density, etc. Additionally, GPR can map soil horizons, the groundwater table, and other anomalies. The prospects of GPR and EMI applications in soil studies need to focus on the potential integration of GPR and EMI to overcome the intrinsic limitations of each technique and enhance their applications to support PA. Future advancements in PA can be strengthened by estimating many soil properties, states, and hydrological processes simultaneously to delineate management zones and calculate optimal inputs in the agricultural landscape. Full article
(This article belongs to the Section Environmental Remote Sensing)
►▼ Show Figures

Graphical abstract

28 pages, 15615 KiB  
Article
Retrieving Atmospheric Gas Profiles Using FY-3E/HIRAS-II Infrared Hyperspectral Data by Neural Network Approach
by Han Li, Mingjian Gu, Chunming Zhang, Mengzhen Xie, Tianhang Yang and Yong Hu
Remote Sens. 2023, 15(11), 2931; https://doi.org/10.3390/rs15112931 - 04 Jun 2023
Cited by 1 | Viewed by 1318
Abstract
The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane [...] Read more.
The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed. Full article
►▼ Show Figures

Graphical abstract

27 pages, 31248 KiB  
Article
A Triangular Grid Filter Method Based on the Slope Filter
by Chuanli Kang, Zitao Lin, Siyi Wu, Yiling Lan, Chongming Geng and Sai Zhang
Remote Sens. 2023, 15(11), 2930; https://doi.org/10.3390/rs15112930 - 04 Jun 2023
Cited by 4 | Viewed by 1117
Abstract
High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving [...] Read more.
High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the filter algorithm, this article proposed a triangular grid filter based on the Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground point extraction. This method is accurate, stable, and achieves the separation of ground points from non-ground points. Firstly, the Slope Filter is used to remove some non-ground points and reduce the error of taking ground points as non-ground points. Secondly, a triangular grid based on the triangular relationship between each point is established, and the violation triangle is determined through the grid; thus, the corresponding violation points are found in the violation triangle. Thirdly, according to the three-point collinear method to extract the regular points, these points are used to extract the regular landmarks by the KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm. Finally, the dispersed points and irregular landmarks are removed by the Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 data samples provided by ISPRS, obtaining an average error of 3.46%. The results show that the algorithm presented in this article has high processing efficiency and accuracy, which can significantly improve the processing efficiency of point cloud data in practical applications. Full article
(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)
►▼ Show Figures

Figure 1

20 pages, 3203 KiB  
Article
Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction
by Mingchen Yuan, Liang Zhang, Yanhua Wang and Chang Han
Remote Sens. 2023, 15(11), 2929; https://doi.org/10.3390/rs15112929 - 04 Jun 2023
Cited by 2 | Viewed by 1119
Abstract
In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, [...] Read more.
In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter. Full article
(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking)
►▼ Show Figures

Figure 1

26 pages, 10301 KiB  
Article
Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance
by Xiangqing Zhang, Yan Feng, Shun Zhang, Nan Wang, Shaohui Mei and Mingyi He
Remote Sens. 2023, 15(11), 2928; https://doi.org/10.3390/rs15112928 - 04 Jun 2023
Cited by 4 | Viewed by 1514
Abstract
Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. [...] Read more.
Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. However, many problems have not been effectively solved in existing remote-vision-based SaR systems, such as the shortage of person samples in SaR scenarios and the low tolerance of small objects for bounding boxes. To address these issues, a copy-paste mechanism (ISCP) with semi-supervised object detection (SSOD) via instance segmentation and maximum mean discrepancy distance is proposed (MMD), which can provide highly robust, multi-task, and efficient aerial-based person detection for the prototype SaR system. Specifically, numerous pseudo-labels are obtained by accurately segmenting the instances of synthetic ISCP samples to obtain their boundaries. The SSOD trainer then uses soft weights to balance the prediction entropy of the loss function between the ground truth and unreliable labels. Moreover, a novel evaluation metric MMD for anchor-based detectors is proposed to elegantly compute the IoU of the bounding boxes. Extensive experiments and ablation studies on Heridal and optimized public datasets demonstrate that our approach is effective and achieves state-of-the-art person detection performance in aerial images. Full article
(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Data Processing)
►▼ Show Figures

Figure 1

20 pages, 7987 KiB  
Article
Multi-Class Double-Transformation Network for SAR Image Registration
by Xiaozheng Deng, Shasha Mao, Jinyuan Yang, Shiming Lu, Shuiping Gou, Youming Zhou and Licheng Jiao
Remote Sens. 2023, 15(11), 2927; https://doi.org/10.3390/rs15112927 - 04 Jun 2023
Cited by 2 | Viewed by 1382
Abstract
In SAR image registration, most existing methods consider the image registration as a two-classification problem to construct the pair training samples for training the deep model. However, it is difficult to obtain a mass of given matched-points directly from SAR images as the [...] Read more.
In SAR image registration, most existing methods consider the image registration as a two-classification problem to construct the pair training samples for training the deep model. However, it is difficult to obtain a mass of given matched-points directly from SAR images as the training samples. Based on this, we propose a multi-class double-transformation network for SAR image registration based on Swin-Transformer. Different from existing methods, the proposed method directly considers each key point as an independent category to construct the multi-classification model for SAR image registration. Then, based on the key points from the reference and sensed images, respectively, a double-transformation network with two branches is designed to search for matched-point pairs. In particular, to weaken the inherent diversity between two SAR images, key points from one image are transformed to the other image, and the transformed image is used as the basic image to capture sub-images corresponding to all key points as the training and testing samples. Moreover, a precise-matching module is designed to increase the reliability of the obtained matched-points by eliminating the inconsistent matched-point pairs given by two branches. Finally, a series of experiments illustrate that the proposed method can achieve higher registration performance compared to existing methods. Full article
(This article belongs to the Special Issue Computational Intelligence for Remote Sensing Image Analysis and Applications)
►▼ Show Figures

Figure 1

18 pages, 8141 KiB  
Article
Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example
by Can Liu, Yu Chen, Yongming Wei and Fang Chen
Remote Sens. 2023, 15(11), 2926; https://doi.org/10.3390/rs15112926 - 03 Jun 2023
Cited by 1 | Viewed by 1983
Abstract
A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are [...] Read more.
A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are many population products derived from remote sensing nighttime light (NTL) and other auxiliary data, they are limited by the coarse spatial resolution of NTL data. As a result, the outcomes’ spatial resolution is restricted, and it cannot meet the requirements of some applications. To address this limitation, this study employs the nighttime light data provided by the SDGSAT-1 satellite, which has a spatial resolution of 10 m, and land use data as auxiliary data to disaggregate the population distribution data from WorldPop data (100 m resolution) to a high resolution of 10 m. The case study conducted in Guilin, China, using the multi-class weighted dasymetric mapping method shows that the total error during the disaggregation is 0.63%, and the accuracy of 146 towns in the study area is represented by an R2 of 0.99. In comparison to the WorldPop data, the result’s information entropy and spatial frequency increases by 345% and 1142%, respectively, which demonstrates the effectiveness of this approach in studying population distributions with high spatial resolution. Full article
(This article belongs to the Special Issue Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation)
►▼ Show Figures

Figure 1

18 pages, 9067 KiB  
Article
Validation of FY-4A Temperature Profiles by Radiosonde Observations in Taklimakan Desert in China
by Yufen Ma, Juanjuan Liu, Ali Mamtimin, Ailiyaer Aihaiti and Lan Xu
Remote Sens. 2023, 15(11), 2925; https://doi.org/10.3390/rs15112925 - 03 Jun 2023
Cited by 3 | Viewed by 1172
Abstract
The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an [...] Read more.
The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region. Full article
(This article belongs to the Special Issue Understanding the Meteorological Environment in Arid Regions through the Integrative Analyses of Remote Sensing, Ground Observational Stations and Numerical Models)
►▼ Show Figures

Figure 1

25 pages, 22522 KiB  
Article
Three-Dimensional Modelling of Past and Present Shahjahanabad through Multi-Temporal Remotely Sensed Data
by Vaibhav Rajan, Mila Koeva, Monika Kuffer, Andre Da Silva Mano and Shubham Mishra
Remote Sens. 2023, 15(11), 2924; https://doi.org/10.3390/rs15112924 - 03 Jun 2023
Cited by 1 | Viewed by 2021
Abstract
Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a [...] Read more.
Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a modern lifestyle, which often results in the loss of cultural heritage. Indian cities such as Delhi possess a rich legacy of tangible heritage, traditions, and arts, which are reflected in their present urban form. The creation of temporal 3D models of such cities not only provides a platform with which one can experience the past, but also helps to understand, examine, and improve its present deteriorating state. However, gaining access to historical data to support the development of city-scale 3D models is a challenge. While data gaps can be bridged by combining multiple data sources, this process also presents considerable technical challenges. This paper provides a framework to generate LoD-2 (level-of-detail) 3D models of the present (the 2020s) and the past (the 1970s) of a heritage mosque surrounded by a dense and complex urban settlement in Shahjahanabad (Old Delhi) by combining multiple VHR (very high resolution) satellite images. The images used are those of Pleiades and Worldview-1 and -3 (for the present) and HEXAGON KH-9 declassified spy images (for the past). The chronological steps are used to extract the DSMs and DTMs that provide a base for the 3D models. The models are rendered, and the past and present are visualized using graphics and videos. The results reveal an average increase of 80% in the heights of the built structures around the main monument (mosque), leading to a loss in the visibility of this central mosque. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research)
►▼ Show Figures

Graphical abstract

20 pages, 5362 KiB  
Article
Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments
by Bin Tan, Zhixiong Ma, Xichan Zhu, Sen Li, Lianqing Zheng, Libo Huang and Jie Bai
Remote Sens. 2023, 15(11), 2923; https://doi.org/10.3390/rs15112923 - 03 Jun 2023
Cited by 1 | Viewed by 1988
Abstract
This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion [...] Read more.
This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Applications in Intelligent Transportation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-252
Previous Issue
Next Issue
Back to TopTop