Pub Date : 2022-09-01DOI: 10.1109/MGRS.2022.3170350
Zhengjia Zhang, Hong Lin, Mengmeng Wang, Xiuguo Liu, Qihao Chen, Chao Wang, Hong Zhang
With climate change and the increment of human activities, global permafrost is undergoing degradation, threatening the stability of engineering and the ecological environment of the permafrost region. With the advantages of all-day, all-weather, wide-coverage, and high-accuracy monitoring, synthetic aperture radar interferometry (InSAR) is becoming a substantial tool for permafrost monitoring, and many studies with InSAR in permafrost regions have been conducted in the recent 20 years. In this article, the basic principles of time–series InSAR are introduced first. Then, the development and applications of InSAR in permafrost, such as the coherence analysis of permafrost ground surface, the deformation of permafrost and infrastructure, and active layer thickness (ALT) retrieval, are given. Next, the existing problems, including temporal decorrelation, atmospheric delay, deformation models, and the effect of soil moisture and phase unwrapping (PU), are discussed.
{"title":"A Review of Satellite Synthetic Aperture Radar Interferometry Applications in Permafrost Regions: Current status, challenges, and trends","authors":"Zhengjia Zhang, Hong Lin, Mengmeng Wang, Xiuguo Liu, Qihao Chen, Chao Wang, Hong Zhang","doi":"10.1109/MGRS.2022.3170350","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3170350","url":null,"abstract":"With climate change and the increment of human activities, global permafrost is undergoing degradation, threatening the stability of engineering and the ecological environment of the permafrost region. With the advantages of all-day, all-weather, wide-coverage, and high-accuracy monitoring, synthetic aperture radar interferometry (InSAR) is becoming a substantial tool for permafrost monitoring, and many studies with InSAR in permafrost regions have been conducted in the recent 20 years. In this article, the basic principles of time–series InSAR are introduced first. Then, the development and applications of InSAR in permafrost, such as the coherence analysis of permafrost ground surface, the deformation of permafrost and infrastructure, and active layer thickness (ALT) retrieval, are given. Next, the existing problems, including temporal decorrelation, atmospheric delay, deformation models, and the effect of soil moisture and phase unwrapping (PU), are discussed.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"93-114"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62493134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/MGRS.2022.3187652
Liang-Jian Deng, G. Vivone, Mercedes Eugenia Paoletti, G. Scarpa, Jiang He, Yongjun Zhang, J. Chanussot, A. Plaza
Machine learning (ML) is influencing the literature in several research fields, often through state-of-the-art approaches. In the past several years, ML has been explored for pansharpening, i.e., an image fusion technique based on the combination of a multispectral (MS) image, which is characterized by its medium/low spatial resolution, and higher-spatial-resolution panchromatic (PAN) data. Thus, ML for pansharpening represents an emerging research line that deserves further investigation. In this article, we go through some powerful and widely used ML-based approaches for pansharpening that have been recently proposed in the related literature. Eight approaches are extensively compared. Implementations of these eight methods, exploiting a common software platform and ML library, are developed for comparison purposes. The ML framework for pansharpening will be freely distributed to the scientific community. Experimental results using data acquired by five commonly used sensors for pansharpening and well-established protocols for performance assessment (both at reduced resolution and at full resolution) are shown. The ML-based approaches are compared with a benchmark consisting of classical and variational optimization (VO)-based methods. The pros and cons of each pansharpening technique, based on the training-by-examples philosophy, are reported together with a broad computational analysis. The toolbox is provided in https://github.com/liangjiandeng/DLPan-Toolbox.
{"title":"Machine Learning in Pansharpening: A benchmark, from shallow to deep networks","authors":"Liang-Jian Deng, G. Vivone, Mercedes Eugenia Paoletti, G. Scarpa, Jiang He, Yongjun Zhang, J. Chanussot, A. Plaza","doi":"10.1109/MGRS.2022.3187652","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3187652","url":null,"abstract":"Machine learning (ML) is influencing the literature in several research fields, often through state-of-the-art approaches. In the past several years, ML has been explored for pansharpening, i.e., an image fusion technique based on the combination of a multispectral (MS) image, which is characterized by its medium/low spatial resolution, and higher-spatial-resolution panchromatic (PAN) data. Thus, ML for pansharpening represents an emerging research line that deserves further investigation. In this article, we go through some powerful and widely used ML-based approaches for pansharpening that have been recently proposed in the related literature. Eight approaches are extensively compared. Implementations of these eight methods, exploiting a common software platform and ML library, are developed for comparison purposes. The ML framework for pansharpening will be freely distributed to the scientific community. Experimental results using data acquired by five commonly used sensors for pansharpening and well-established protocols for performance assessment (both at reduced resolution and at full resolution) are shown. The ML-based approaches are compared with a benchmark consisting of classical and variational optimization (VO)-based methods. The pros and cons of each pansharpening technique, based on the training-by-examples philosophy, are reported together with a broad computational analysis. The toolbox is provided in https://github.com/liangjiandeng/DLPan-Toolbox.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"279-315"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44945696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/mgrs.2022.3201428
D. Kunkee
{"title":"To Travel or Not to Travel: The Hybrid Conference Era [President’s Message]","authors":"D. Kunkee","doi":"10.1109/mgrs.2022.3201428","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3201428","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45620346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/mgrs.2022.3201456
J. Garrison
{"title":"Welcome to the September 2022 Issue of IEEE Geoscience and Remote Sensing Magazine! [From the Guest Editors]","authors":"J. Garrison","doi":"10.1109/mgrs.2022.3201456","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3201456","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45277990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/mgrs.2022.3198313
Alberto Moreira, J. Judge, F. Bovolo, A. Plaza
{"title":"IGARSS 2022 in Kuala Lumpur, Malaysia: Impressions of the First Days [Conference Reports]","authors":"Alberto Moreira, J. Judge, F. Bovolo, A. Plaza","doi":"10.1109/mgrs.2022.3198313","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3198313","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49087984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/MGRS.2022.3186904
Linlin Zhang, Guili Gao, Chao Chen, S. Gao, Libo Yao
In recent years, compact polarimetric (CP) SAR has been widely used for Earth target detection as a means to balance system resources and target information. Although there has been a large number of related researches, an in-depth review of CP SAR, from basic principles to target detection methods, is lacking. This article aims to provide a review of this area. In this article, we review the historical development and application status of CP SAR, introduce the basic principles and the target detection principles of CP SAR, and summarize the currently proposed methods for CP SAR target detection. Over 200 publications are covered. We also discuss the challenges of current research and point out three promising research directions, i.e., the further deepening of feature extraction, deeper understanding of target characteristics, and development of intelligent detection techniques. We hope that this survey will help researchers to better understand this research area.
{"title":"Compact Polarimetric Synthetic Aperture Radar for Target Detection: A review","authors":"Linlin Zhang, Guili Gao, Chao Chen, S. Gao, Libo Yao","doi":"10.1109/MGRS.2022.3186904","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3186904","url":null,"abstract":"In recent years, compact polarimetric (CP) SAR has been widely used for Earth target detection as a means to balance system resources and target information. Although there has been a large number of related researches, an in-depth review of CP SAR, from basic principles to target detection methods, is lacking. This article aims to provide a review of this area. In this article, we review the historical development and application status of CP SAR, introduce the basic principles and the target detection principles of CP SAR, and summarize the currently proposed methods for CP SAR target detection. Over 200 publications are covered. We also discuss the challenges of current research and point out three promising research directions, i.e., the further deepening of feature extraction, deeper understanding of target characteristics, and development of intelligent detection techniques. We hope that this survey will help researchers to better understand this research area.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"115-152"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49535921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/MGRS.2022.3171836
Maria Sdraka, I. Papoutsis, Bill Psomas, Konstantinos Vlachos, K. Ioannidis, K. Karantzalos, Ilias Gialampoukidis, S. Vrochidis
The past few years have seen an accelerating integration of deep learning (DL) techniques into various remote sensing (RS) applications, highlighting their power to adapt and achieving unprecedented advancements. In the present review, we provide an exhaustive exploration of the DL approaches proposed specifically for the spatial downscaling of RS imagery. A key contribution of our work is the presentation of the major architectural components and models, metrics, and data sets available for this task as well as the construction of a compact taxonomy for navigating through the various methods. Furthermore, we analyze the limitations of the current modeling approaches and provide a brief discussion on promising directions for image enhancement, following the paradigm of general computer vision (CV) practitioners and researchers as a source of inspiration and constructive insight.
{"title":"Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution","authors":"Maria Sdraka, I. Papoutsis, Bill Psomas, Konstantinos Vlachos, K. Ioannidis, K. Karantzalos, Ilias Gialampoukidis, S. Vrochidis","doi":"10.1109/MGRS.2022.3171836","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3171836","url":null,"abstract":"The past few years have seen an accelerating integration of deep learning (DL) techniques into various remote sensing (RS) applications, highlighting their power to adapt and achieving unprecedented advancements. In the present review, we provide an exhaustive exploration of the DL approaches proposed specifically for the spatial downscaling of RS imagery. A key contribution of our work is the presentation of the major architectural components and models, metrics, and data sets available for this task as well as the construction of a compact taxonomy for navigating through the various methods. Furthermore, we analyze the limitations of the current modeling approaches and provide a brief discussion on promising directions for image enhancement, following the paradigm of general computer vision (CV) practitioners and researchers as a source of inspiration and constructive insight.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"202-255"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44593212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-27DOI: 10.1109/MGRS.2022.3198244
Yi Wang, C. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu
In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.
{"title":"Self-Supervised Learning in Remote Sensing: A review","authors":"Yi Wang, C. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu","doi":"10.1109/MGRS.2022.3198244","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3198244","url":null,"abstract":"In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"213-247"},"PeriodicalIF":14.6,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49242607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2021.3115448
N. Pierdicca, D. Comite, Adriano Camps, H. Carreno-Luengo, Luca Cenci, M. Clarizia, Fabiano Costantini, L. Dente, L. Guerriero, Antonio Mmollfulleda, S. Paloscia, Hyuk Park, E. Santi, M. Zribi, N. Floury
This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS-R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflectivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The encouraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks.
{"title":"The Potential of Spaceborne GNSS Reflectometry for Soil Moisture, Biomass, and Freeze–Thaw Monitoring: Summary of a European Space Agency-funded study","authors":"N. Pierdicca, D. Comite, Adriano Camps, H. Carreno-Luengo, Luca Cenci, M. Clarizia, Fabiano Costantini, L. Dente, L. Guerriero, Antonio Mmollfulleda, S. Paloscia, Hyuk Park, E. Santi, M. Zribi, N. Floury","doi":"10.1109/mgrs.2021.3115448","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3115448","url":null,"abstract":"This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS-R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflectivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The encouraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"8-38"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45267110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2021.3136100
C. Persello, J. D. Wegner, Ronny Hansch, D. Tuia, Pedram Ghamisi, M. Koeva, Gustau Camps-Valls
The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.
{"title":"Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities","authors":"C. Persello, J. D. Wegner, Ronny Hansch, D. Tuia, Pedram Ghamisi, M. Koeva, Gustau Camps-Valls","doi":"10.1109/mgrs.2021.3136100","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3136100","url":null,"abstract":"The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"172-200"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44363667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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