Pub Date : 2021-12-01DOI: 10.1109/MGRS.2020.3032575
Han Zhai, Hongyan Zhang, Pingxiang Li, Liangpei Zhang
Hyperspectral remote sensing organically combines traditional space imaging with advanced spectral measurement technologies, delivering advantages stemming from continuous spectrum data and rich spatial information. This development of hyperspectral technology takes remote sensing into a brand-new phase, making the technology widely applicable in various fields. Hyperspectral clustering analysis is widely utilized in hyperspectral image (HSI) interpretation and information extraction, which can reveal the natural partition pattern of pixels in an unsupervised way. In this article, current hyperspectral clustering algorithms are systematically reviewed and summarized in nine main categories: centroid-based, density-based, probability-based, bionics-based, intelligent computing-based, graph-based, subspace clustering, deep learning-based, and hybrid mechanism-based. The performance of several popular hyperspectral clustering methods is demonstrated on two widely used data sets. HSI clustering challenges and possible future research lines are identified.
{"title":"Hyperspectral Image Clustering: Current achievements and future lines","authors":"Han Zhai, Hongyan Zhang, Pingxiang Li, Liangpei Zhang","doi":"10.1109/MGRS.2020.3032575","DOIUrl":"https://doi.org/10.1109/MGRS.2020.3032575","url":null,"abstract":"Hyperspectral remote sensing organically combines traditional space imaging with advanced spectral measurement technologies, delivering advantages stemming from continuous spectrum data and rich spatial information. This development of hyperspectral technology takes remote sensing into a brand-new phase, making the technology widely applicable in various fields. Hyperspectral clustering analysis is widely utilized in hyperspectral image (HSI) interpretation and information extraction, which can reveal the natural partition pattern of pixels in an unsupervised way. In this article, current hyperspectral clustering algorithms are systematically reviewed and summarized in nine main categories: centroid-based, density-based, probability-based, bionics-based, intelligent computing-based, graph-based, subspace clustering, deep learning-based, and hybrid mechanism-based. The performance of several popular hyperspectral clustering methods is demonstrated on two widely used data sets. HSI clustering challenges and possible future research lines are identified.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"9 1","pages":"35-67"},"PeriodicalIF":14.6,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MGRS.2020.3032575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49353878","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 : 2021-12-01DOI: 10.1109/mgrs.2021.3120892
P. de Matthaeis
{"title":"Agenda Items of the World Radiocommunication Conference 2023 With a Potential Impact on Microwave Remote Sensing [Technical Committees]","authors":"P. de Matthaeis","doi":"10.1109/mgrs.2021.3120892","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3120892","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45397233","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 : 2021-10-25DOI: 10.1109/MGRS.2021.3115137
Xin Wu, Wei Li, D. Hong, R. Tao, Qian Du
Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.
{"title":"Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey","authors":"Xin Wu, Wei Li, D. Hong, R. Tao, Qian Du","doi":"10.1109/MGRS.2021.3115137","DOIUrl":"https://doi.org/10.1109/MGRS.2021.3115137","url":null,"abstract":"Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"91-124"},"PeriodicalIF":14.6,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41389892","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 : 2021-09-13DOI: 10.36227/techrxiv.14828256.v2
A. Lucas, É. Gayer
On the seasonal timescale, for accessible locations and when manpower is available, direct observations and field surveys are the most useful and standard approaches. However very limited studies have been conducted through direct examination on decennial-to-century timescales due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (across a timeline of up to 70 years) that includes sensor calibration, dense matching, and elevation reconstruction in two areas of interest that represent pristine examples of tropical and alpine environments. The Remparts and Langevin rivers, on Réunion Island, and the Bossons glacier, in the French Alps, share limited accessibility (in time and space) that can be overcome only by remote sensing. We reach a metric-to-submetric resolution close to the nominal image spatial sampling. This provides elevation time series with better resolution than most recent satellite images, such as Pleiades, in a decennial time period.
{"title":"Decennial Geomorphic Transport From Archived Time Series Digital Elevation Models: A cookbook for tropical and alpine environments","authors":"A. Lucas, É. Gayer","doi":"10.36227/techrxiv.14828256.v2","DOIUrl":"https://doi.org/10.36227/techrxiv.14828256.v2","url":null,"abstract":"On the seasonal timescale, for accessible locations and when manpower is available, direct observations and field surveys are the most useful and standard approaches. However very limited studies have been conducted through direct examination on decennial-to-century timescales due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (across a timeline of up to 70 years) that includes sensor calibration, dense matching, and elevation reconstruction in two areas of interest that represent pristine examples of tropical and alpine environments. The Remparts and Langevin rivers, on Réunion Island, and the Bossons glacier, in the French Alps, share limited accessibility (in time and space) that can be overcome only by remote sensing. We reach a metric-to-submetric resolution close to the nominal image spatial sampling. This provides elevation time series with better resolution than most recent satellite images, such as Pleiades, in a decennial time period.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"120-134"},"PeriodicalIF":14.6,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47423125","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 : 2021-09-01DOI: 10.1109/MGRS.2021.3051859
D. S. Maia, M. Pham, E. Aptoula, Florent Guiotte, S. Lefèvre
Morphological attribute profiles (APs) are among the most prominent methods for spatial–spectral pixel analysis of remote sensing images. Since their introduction a decade ago to tackle land cover classification, many studies have been contributed to the state of the art, focusing not only on their application to a wider range of tasks but also on their performance improvement and extension to more complex Earth observation data.
{"title":"Classification of Remote Sensing Data With Morphological Attribute Profiles: A decade of advances","authors":"D. S. Maia, M. Pham, E. Aptoula, Florent Guiotte, S. Lefèvre","doi":"10.1109/MGRS.2021.3051859","DOIUrl":"https://doi.org/10.1109/MGRS.2021.3051859","url":null,"abstract":"Morphological attribute profiles (APs) are among the most prominent methods for spatial–spectral pixel analysis of remote sensing images. Since their introduction a decade ago to tackle land cover classification, many studies have been contributed to the state of the art, focusing not only on their application to a wider range of tasks but also on their performance improvement and extension to more complex Earth observation data.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"9 1","pages":"43-71"},"PeriodicalIF":14.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MGRS.2021.3051859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43657854","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 : 2021-09-01DOI: 10.1109/mgrs.2021.3100654
M. Schmitt, C. Persello, G. Vivone, D. Lunga, Wenzhi Liao, N. Yokoya, Pedram Ghamisi, R. Hänsch
{"title":"The New Working Groups of the GRSS Technical Committee on Image Analysis and Data Fusion [Technical Committees]","authors":"M. Schmitt, C. Persello, G. Vivone, D. Lunga, Wenzhi Liao, N. Yokoya, Pedram Ghamisi, R. Hänsch","doi":"10.1109/mgrs.2021.3100654","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3100654","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49356969","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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