Research

19 pages, 2897 KiB

Open AccessArticle

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

by Ziming Ni, Xianzhong Chen, Qingwen Hou and Jie Zhang

Remote Sens. 2024, 16(9), 1509; https://doi.org/10.3390/rs16091509 - 25 Apr 2024

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The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna [...] Read more.

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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16 pages, 10343 KiB

Open AccessArticle

Solar Wind Charge-Exchange X-ray Emissions from the O⁵⁺ Ions in the Earth’s Magnetosheath

by Zhicheng Zhang, Fei He, Xiao-Xin Zhang, Guiyun Liang, Xueyi Wang and Yong Wei

Remote Sens. 2024, 16(9), 1480; https://doi.org/10.3390/rs16091480 - 23 Apr 2024

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Abstract

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O⁶⁺ ions, which are the [...] Read more.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O⁶⁺ ions, which are the primary charge state for oxygen ions in solar wind, are considered. The line emissivity of the charge-exchange-borne O⁵⁺ ions is calculated by the Spectral Analysis System for Astrophysical and Laboratory (SASAL). It is found that the emission lines from O⁵⁺ range from 105.607 to 118.291 eV with a strong line at 107.047 eV. We then simulate the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of the north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm⁻² s⁻¹ sr⁻¹ on the dayside, which is 4.5 times that before the solar wind structure reached the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at the north pole. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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29 pages, 17809 KiB

Open AccessArticle

Revealing Decadal Glacial Changes and Lake Evolution in the Cordillera Real, Bolivia: A Semi-Automated Landsat Imagery Analysis

by Yilin Huang and Tsuyoshi Kinouchi

Remote Sens. 2024, 16(7), 1231; https://doi.org/10.3390/rs16071231 - 31 Mar 2024

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Abstract

The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the [...] Read more.

The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the cloud platforms Google Earth Engine and Google Colab to effectively detect dynamic changes in the glaciers as well as glacial and non-glacial lakes of the Cordillera Real, Bolivia, using over 200 Landsat images from 1984 to 2021. We found that the study area experienced a rise in temperature and precipitation, resulting in a substantial decline in glacier coverage and a simultaneous increase in both the total number and total area of lakes. A strong correlation between glacier area and the extent of natural glacier-fed lakes highlights the significant downstream impact of glacier recession on water bodies. Over the study period, glaciers reduced their total area by 42%, with recent years showing a deceleration in glacier recession, aligning with the recent stabilization observed in the area of natural glacier-fed lakes. Despite these overall trends, many smaller lakes, especially non-glacier-fed ones, decreased in size, attributed to seasonal and inter-annual variations in lake inflow caused by climate variability. These findings suggest the potential decline of natural lakes amid ongoing climate changes, prompting alterations in natural landscapes and local water resources. The study reveals the response of glaciers and lakes to climate variations, including the contribution of human-constructed water reservoirs, providing valuable insights into crucial aspects of future water resources in the Cordillera Real. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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29 pages, 11112 KiB

Open AccessArticle

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

by Valerio Pampanoni, Fabio Fascetti, Luca Cenci, Giovanni Laneve, Carla Santella and Valentina Boccia

Remote Sens. 2024, 16(6), 1041; https://doi.org/10.3390/rs16061041 - 15 Mar 2024

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Abstract

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of [...] Read more.

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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24 pages, 4327 KiB

Open AccessArticle

Dynamic Spatial–Spectral Feature Optimization-Based Point Cloud Classification

by Yali Zhang, Wei Feng, Yinghui Quan, Guangqiang Ye and Gabriel Dauphin

Remote Sens. 2024, 16(3), 575; https://doi.org/10.3390/rs16030575 - 02 Feb 2024

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Abstract

With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational [...] Read more.

With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial–spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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Graphical abstract

24 pages, 8438 KiB

Open AccessArticle

Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices

by Sarah Farhadpour, Timothy A. Warner and Aaron E. Maxwell

Remote Sens. 2024, 16(3), 533; https://doi.org/10.3390/rs16030533 - 30 Jan 2024

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Abstract

Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore [...] Read more.

Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore key considerations in selecting and interpreting loss and assessment metrics in the context of data imbalance, which arises when the classes have unequal proportions within the dataset or landscape being mapped. The challenges involved in calculating single, integrated measures that summarize classification success, especially for datasets with considerable data imbalance, have led to much confusion in the literature. This confusion arises from a range of issues, including a lack of clarity over the redundancy of some accuracy measures, the importance of calculating final accuracy from population-based statistics, the effects of class imbalance on accuracy statistics, and the differing roles of accuracy measures when used for training and final evaluation. In order to characterize classification success at the class level, users typically generate averages from the class-based measures. These averages are sometimes generated at the macro-level, by taking averages of the individual-class statistics, or at the micro-level, by aggregating values within a confusion matrix, and then, calculating the statistic. We show that the micro-averaged producer’s accuracy (recall), user’s accuracy (precision), and F1-score, as well as weighted macro-averaged statistics where the class prevalences are used as weights, are all equivalent to each other and to the overall accuracy, and thus, are redundant and should be avoided. Our experiment, using a variety of loss metrics for training, suggests that the choice of loss metric is not as complex as it might appear to be, despite the range of choices available, which include cross-entropy (CE), weighted CE, and micro- and macro-Dice. The highest, or close to highest, accuracies in our experiments were obtained by using CE loss for models trained with balanced data, and for models trained with imbalanced data, the highest accuracies were obtained by using weighted CE loss. We recommend that, since weighted CE loss used with balanced training is equivalent to CE, weighted CE loss is a good all-round choice. Although Dice loss is commonly suggested as an alternative to CE loss when classes are imbalanced, micro-averaged Dice is similar to overall accuracy, and thus, is particularly poor for training with imbalanced data. Furthermore, although macro-Dice resulted in models with high accuracy when the training used balanced data, when the training used imbalanced data, the accuracies were lower than for weighted CE. In summary, the significance of this paper lies in its provision of readers with an overview of accuracy and loss metric terminology, insight regarding the redundancy of some measures, and guidance regarding best practices. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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19 pages, 4581 KiB

Open AccessArticle

An Efficient Rep-Style Gaussian–Wasserstein Network: Improved UAV Infrared Small Object Detection for Urban Road Surveillance and Safety

by Tuerniyazi Aibibu, Jinhui Lan, Yiliang Zeng, Weijian Lu and Naiwei Gu

Remote Sens. 2024, 16(1), 25; https://doi.org/10.3390/rs16010025 - 20 Dec 2023

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Abstract

Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein [...] Read more.

Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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28 pages, 4722 KiB

Open AccessArticle

Hyperspectral Target Detection Methods Based on Statistical Information: The Key Problems and the Corresponding Strategies

by Luyan Ji and Xiurui Geng

Remote Sens. 2023, 15(15), 3835; https://doi.org/10.3390/rs15153835 - 01 Aug 2023

Viewed by 1094

Abstract

Target detection is an important area in the applications of hyperspectral remote sensing. Due to the full use of information of the target and background, target detection algorithms based on the statistical characteristics of an image are always occupy a dominant position in [...] Read more.

Target detection is an important area in the applications of hyperspectral remote sensing. Due to the full use of information of the target and background, target detection algorithms based on the statistical characteristics of an image are always occupy a dominant position in the field of hyperspectral target detection. From the perspective of statistical information, we firstly presented detailed discussions on the key factors affecting the target detection results, including data origin, target size, spectral variability of target, and the number of bands. Further, we gave the corresponding strategies for several common situations in the practical target detection applications. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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