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Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 35021

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Special Issue Editors

Dr. Alireza Taravat

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Deimos Space UK Ltd., Building R103, Fermi Avenue, Harwell, Oxford OX11 0QR, UK
Interests: neural networks; image processing; remote sensing; modelling; Imaging spectroscopy; hydrology; water management; image fusion; drought monitoring; PCNN; anthropogenic activities; long-term change detection; wetland identification
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Dr. Naoto Yokoya

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RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: remote sensing; image processing; data fusion; machine learning; disaster management; environmental monitoring
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Prof. Dr. Jon Atli Benediktsson

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Faculty of Electrical and Computer Engineering, University of Iceland, IS 102 Reykjavík, Iceland
Interests: remote sensing; image analysis; pattern recognition; signal processing
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Prof. Hongjun Su

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School of Earth Science and Engineering, Hohai University, 8 Focheng West Road, Jiangning District, Nanjing 211100, China
Interests: hyperspectral remote sensing; image analysis; machine learning; computational intelligence
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Prof. Dr. Cristina Rubio-Escudero

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Department of Computer Languages and Systems, University of Sevillal, Avda. Reina Mercedes s/n, 41012 Sevilla, Spain
Interests: data science; data mining

Prof. Dr. Antonio Morales Esteban

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Department of Building Structures and Geotechnical Engineering, University of Seville, 41002 Seville, Spain
Interests: seismic hazard; sustainability; seismic vulnerability; retrofitting; seismic zoning; signal processing; earthquake prediction; soil-structure interaction; dynamic analysis; historical seismicity; heritage
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Dr. José L. Amaro-Mellado

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1. Instituto Geográfico Nacional (National Geographic Institute) of Spain, Andalusian Division, 41013 Seville, Spain
2. Department of Graphic Engineering, University of Seville, 41012 Seville, Spain
Interests: geomatics; GIS; geosciences; natural hazards; seismology; engineering education; remote sensing
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Prof. Dr. Francisco Martínez-Álvarez

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Department of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: big data; machine learning; time series; forecasting
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Prof. Dr. Ata Jahangir Moshayedi

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School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
Interests: machine vision; remote sensing

Prof. Dr. Biplab Banerjee

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IIT Bombay, Powai, Mumbai 400076, India
Interests: deep learning; computer vision

Dr. Mercedes E. Paoletti

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Department of Computer Technology and Communications, Polytechnic School of Cáceres, University of Extremadura, avenida de la Universidad s/n, 10003 Cáceres, Spain
Interests: hyperspectral remote sensing; deep learning; Graphics Processing Units (GPUs); High Performance Computing (HPC) techniques
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Special Issue Information

Dear Colleagues,

Current and future high-resolution satellite earth observation missions will provide data coverage that has never been available before and with a largely untapped potential. High-resolution hyperspectral and LiDAR sensors are also gaining attention as they become cheaper in operation and more suitable for use on UAVs or small satellites. This extends the traditional set of multispectral optical and SAR imagery to new fields of application. However, there is still a lack of advance models for manipulation and exploitation of new earth observation big data. On the other hand, imagery analytics and interpretation, which are often still performed by human experts, require an increase in the level of automation in the process of value-added generation from data. Hence, powerful data mining algorithms are required to mine useful information. Even with so much literature devoted to this topic, there is still so much we do not know about machine learning models in the remote sensing field. This Special Issue aims to foster the application of advanced machine learning and deep learning algorithms to remote sensing problems. The scope is broad, but contributions with a sufficiently specific focus are preferred.

For this Special Issue, we welcome contributions related to:

Understanding of advanced ML and DL architecture for Earth Observation data analysis;
Transfer learning, cross-sensor learning;
DL model fusion;
Advanced ML models for high-resolution RS image segmentation and classification;
High-resolution RS data fusion (Optical, SAR, and LiDAR) using ML models;
High-resolution RS time-series analysis using ML and DL models.

Dr. Alireza Taravat
Dr. Naoto Yokoya
Prof. Jon Atli Benediktsson
Prof. Hongjun Su
Prof. Cristina Rubio-Escudero
Dr. Antonio Morales Esteban
Dr. José L. Amaro-Mellado
Prof. Francisco Martínez-Álvarez
Prof. Ata Jahangir Moshayedi
Prof. Biplab Banerjee
Ms. Mercedes Paoletti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
Deep learning
Machine learning
Image processing
Transfer learning
Automatic onboard processing
Convolutional neural network
Recurrent neural network

Published Papers (7 papers)

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21 pages, 35766 KiB

Open AccessArticle

Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images

by Teerapong Panboonyuen, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern and Peerapon Vateekul

Remote Sens. 2021, 13(24), 5100; https://doi.org/10.3390/rs13245100 - 15 Dec 2021

Cited by 30 | Viewed by 5299

Abstract

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8%

F 1

score), Thailand North Landsat-8 corpus (63.12%

F 1

score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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20 pages, 8179 KiB

Open AccessArticle

Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method

by Zhiwei Chen, Wei Zheng, Wenjie Yin, Xiaoping Li, Gangqiang Zhang and Jing Zhang

Remote Sens. 2021, 13(23), 4760; https://doi.org/10.3390/rs13234760 - 24 Nov 2021

Cited by 16 | Viewed by 2597

Abstract

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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28 pages, 11976 KiB

Open AccessArticle

Sequence Image Datasets Construction via Deep Convolution Networks

by Xing Jin, Ping Tang and Zheng Zhang

Remote Sens. 2021, 13(9), 1853; https://doi.org/10.3390/rs13091853 - 10 May 2021

Cited by 2 | Viewed by 2298

Abstract

Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors such as cloud noise for optical data. Image transformation is the method that is often used to deal with this issue. This paper considers the deep convolution networks to learn the complex mapping between sequence images, called adaptive filter generation network (AdaFG), convolution long short-term memory network (CLSTM), and cycle-consistent generative adversarial network (CyGAN) for construction of sequence image datasets. AdaFG network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. CLSTM network can map between different images using the state information of multiple time-series images. CyGAN network can map an image from a source domain to a target domain without additional information. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the deep convolution networks are effective to produce high-quality time-series image datasets, and the data-driven deep convolution networks can better simulate complex and diverse nonlinear data information. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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21 pages, 8861 KiB

Open AccessArticle

Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

by Chih-Chiang Wei and Chen-Chia Hsu

Remote Sens. 2020, 12(14), 2203; https://doi.org/10.3390/rs12142203 - 09 Jul 2020

Cited by 10 | Viewed by 2329

Abstract

The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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29 pages, 14114 KiB

Open AccessEditor’s ChoiceArticle

How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

by Xin Zhang, Liangxiu Han, Lianghao Han and Liang Zhu

Remote Sens. 2020, 12(3), 417; https://doi.org/10.3390/rs12030417 - 28 Jan 2020

Cited by 97 | Viewed by 12077

Abstract

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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25 pages, 12733 KiB

Open AccessArticle

Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment

by Alireza Arabameri, Wei Chen, Luigi Lombardo, Thomas Blaschke and Dieu Tien Bui

Remote Sens. 2020, 12(1), 140; https://doi.org/10.3390/rs12010140 - 01 Jan 2020

Cited by 31 | Viewed by 3646

Abstract

Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher’s linear discriminant analysis (FLDA), logistic model tree (LMT) and naïve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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11 pages, 7148 KiB

Open AccessTechnical Note

Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection

by Alireza Taravat, Matthias P. Wagner, Rogerio Bonifacio and David Petit

Remote Sens. 2021, 13(4), 722; https://doi.org/10.3390/rs13040722 - 16 Feb 2021

Cited by 23 | Viewed by 4530

Abstract

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F₁ score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F₁ score of 0.88 and an average Jaccard coefficient of 0.77. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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