Pub Date : 2023-12-28DOI: 10.1109/mgrs.2023.3343623
Haoyu Wang, Xiaofeng Li
In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.
{"title":"DeepBlue: Advanced convolutional neural network applications for ocean remote sensing","authors":"Haoyu Wang, Xiaofeng Li","doi":"10.1109/mgrs.2023.3343623","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3343623","url":null,"abstract":"In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071693","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 : 2023-12-21DOI: 10.1109/mgrs.2023.3335631
Mariko Burgin
How time flies! With the end of 2023 (and the first year of my presidency) approaching, it is an opportune time to reflect on 2023 and look ahead to 2024 (and the second [and last] year of my presidency).
{"title":"Letter From the President [President’s Message]","authors":"Mariko Burgin","doi":"10.1109/mgrs.2023.3335631","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3335631","url":null,"abstract":"How time flies! With the end of 2023 (and the first year of my presidency) approaching, it is an opportune time to reflect on 2023 and look ahead to 2024 (and the second [and last] year of my presidency).","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659887","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 : 2023-12-21DOI: 10.1109/mgrs.2023.3338312
{"title":"Tech RXIV: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/mgrs.2023.3338312","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3338312","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204945","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 : 2023-12-01DOI: 10.1109/mgrs.2023.3314450
L. Karthikeyan, A. Bhattacharya, J. Judge, S. Yueh
{"title":"The Second International Soil Moisture School [Conference Reports]","authors":"L. Karthikeyan, A. Bhattacharya, J. Judge, S. Yueh","doi":"10.1109/mgrs.2023.3314450","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3314450","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017928","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 : 2023-12-01DOI: 10.1109/mgrs.2023.3302342
R. Hänsch, C. Persello, G. Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun
{"title":"Report on the 2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]","authors":"R. Hänsch, C. Persello, G. Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun","doi":"10.1109/mgrs.2023.3302342","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3302342","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139023550","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 : 2023-12-01DOI: 10.1109/MGRS.2023.3321333
Nirav Patel
Synthetic aperture radar (SAR) imaging data in general have not been openly accessible for consumption to the general public in the past few decades, as mainly governments have led the development of such platforms, due to the commercial industry lacking the need of such data (with few exceptions).
{"title":"Open Source Data Programs From Low-Earth Orbit Synthetic Aperture Radar Companies: Questions and answers [Industry Profiles and Activities]","authors":"Nirav Patel","doi":"10.1109/MGRS.2023.3321333","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3321333","url":null,"abstract":"Synthetic aperture radar (SAR) imaging data in general have not been openly accessible for consumption to the general public in the past few decades, as mainly governments have led the development of such platforms, due to the commercial industry lacking the need of such data (with few exceptions).","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015531","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 : 2023-12-01DOI: 10.1109/MGRS.2023.3321479
Stefan Podaru, Guillem Gracia-Sola, Adriano Camps
CubeSats are now serving a wide range of applications beyond their original educational intent. Private companies are deploying large constellations for Earth observation and machine–to–machine communications. Their growing popularity and increased performance have raised the demand for reliability and costs. Today, it is becoming increasingly difficult to find subsystems providers, and the trend is to find fully integrated platforms on the market. Therefore, paradoxically, CubeSats are becoming less accessible to universities and research institutions than a few years ago. To overcome these problems, the PocketQube concept was invented. PocketQubes measure 50 × 50 × 50 mm³ and offer a cost-effective option, notably for education. These picosatellites can perform simple missions like Internet of Things communications or upper-atmosphere observations, ionosphere studies, or signal integrity tasks, while students face design challenges similar to larger satellites. This article presents the IEEE Geoscience and Remote Sensing Society (GRSS) “Open PocketQube Kit” educational initiative. Developed by the NanoSat Lab at the Polytechnic University of Catalonia (UPC), it is an affordable open source educational kit featuring a complete PocketQube structure with all the subsystems: an electrical power supply (EPS), attitude determination and control system (ADCS), an STM32-based onboard computer (OBC), long-range (LoRa) communications, and payload. Three different PocketQube models have been developed: PoCat-1, with a video graphics array (VGA) camera, and PoCat-2 and PoCat-3 for monitoring radio-frequency interference (RFI) at the L-band (1–2 GHz) and K-band (24–25 GHz) to track 5G spectrum emissions.
{"title":"The IEEE Geoscience and Remote Sensing Society “Open PocketQube Kit”: An affordable open source approach to Earth observation missions [Education in Remote Sensing]","authors":"Stefan Podaru, Guillem Gracia-Sola, Adriano Camps","doi":"10.1109/MGRS.2023.3321479","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3321479","url":null,"abstract":"CubeSats are now serving a wide range of applications beyond their original educational intent. Private companies are deploying large constellations for Earth observation and machine–to–machine communications. Their growing popularity and increased performance have raised the demand for reliability and costs. Today, it is becoming increasingly difficult to find subsystems providers, and the trend is to find fully integrated platforms on the market. Therefore, paradoxically, CubeSats are becoming less accessible to universities and research institutions than a few years ago. To overcome these problems, the PocketQube concept was invented. PocketQubes measure 50 × 50 × 50 mm³ and offer a cost-effective option, notably for education. These picosatellites can perform simple missions like Internet of Things communications or upper-atmosphere observations, ionosphere studies, or signal integrity tasks, while students face design challenges similar to larger satellites. This article presents the IEEE Geoscience and Remote Sensing Society (GRSS) “Open PocketQube Kit” educational initiative. Developed by the NanoSat Lab at the Polytechnic University of Catalonia (UPC), it is an affordable open source educational kit featuring a complete PocketQube structure with all the subsystems: an electrical power supply (EPS), attitude determination and control system (ADCS), an STM32-based onboard computer (OBC), long-range (LoRa) communications, and payload. Three different PocketQube models have been developed: PoCat-1, with a video graphics array (VGA) camera, and PoCat-2 and PoCat-3 for monitoring radio-frequency interference (RFI) at the L-band (1–2 GHz) and K-band (24–25 GHz) to track 5G spectrum emissions.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139020652","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 : 2023-12-01DOI: 10.1109/mgrs.2023.3316129
Delwyn Moller, Catherine Qualtrough, Scott Gleason, Scott Hensley, M. Moghaddam, Andrew O’Brien, Brian Pollard, Wolfgang Rack, C. Ruf, Michelangelo Villano
{"title":"The Instrumentation and Future Technology Technical Committee’s Second “Summer School”: Auckland, New Zealand [Technical Committees]","authors":"Delwyn Moller, Catherine Qualtrough, Scott Gleason, Scott Hensley, M. Moghaddam, Andrew O’Brien, Brian Pollard, Wolfgang Rack, C. Ruf, Michelangelo Villano","doi":"10.1109/mgrs.2023.3316129","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3316129","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138987214","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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