IEEE Geoscience and Remote Sensing Magazine最新文献

{"title":"IEEE App","authors":"","doi":"10.1109/mgrs.2023.3338311","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3338311","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"269 1","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138989787","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}

{"title":"Why Does the GRSS Need a Magazine? [From the Editor]","authors":"P. Gamba","doi":"10.1109/mgrs.2023.3329915","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3329915","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"18 ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021233","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}

{"title":"Call for Papers Special issue on “The year of SAR”","authors":"","doi":"10.1109/mgrs.2023.3326604","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3326604","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"38 1","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992857","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}

{"title":"Polarimetric Roll-Invariant Features and Applications for Polarimetric Synthetic Aperture Radar Ship Detection: A comprehensive summary and investigation","authors":"Si-Wei Chen, Ming-Dian Li, Xing-Chao Cui, Hao-Liang Li","doi":"10.1109/mgrs.2023.3328472","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3328472","url":null,"abstract":"Polarimetric radar, which can acquire full polarization information, has become mainstream in microwave remote sensing. However, radar target scattering responses are strongly orientation dependent, which makes applications such as target detection and recognition more difficult. Polarimetric roll-invariant features, which are independent of target orientations along the radar line of sight (LOS), exhibit great importance and have achieved successful applications in many fields. During the development of radar polarimetry, a number of polarimetric roll-invariant features have been found and reported. A comprehensive summary and investigation of these polarimetric roll-invariant features is necessary and valuable for current studies and further developments. This article is dedicated to this purpose and contains two main contributions. First, according to the authors’ knowledge, polarimetric roll-invariant features are thoroughly investigated. Based on their original derivation fashion, they are partitioned into six groups, including those derived from the Sinclair matrix, the Graves matrix, the polarimetric coherency and covariance matrices, eigenvalue–eigenvector-based decompositions, polarimetric coherence/correlation patterns, and the Kennaugh matrix. Meanwhile, their expressions, inner relationships, and physical interpretations are further examined and presented. Second, since ship detection is an important application for polarimetric synthetic aperture radar (PolSAR), the potential of these independent features for PolSAR ship detection is investigated and demonstrated. Experimental studies with <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Radar Satellite-2</i> (\u0000<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">RADARSAT-2</i>\u0000) and <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Gaofen-3</i> PolSAR datasets are carried out. It is validated that the selected polarimetric roll-invariant features exhibit superior performance compared with normal polarimetric features.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"2014 1","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071620","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}

{"title":"Evolutionary Developments of Today’s Remote Sensing Radar Technology—Right From the Telemobiloscope: A review","authors":"Samedh Sachin Kari, A Arockia Bazil Raj, Balasubramanian. K","doi":"10.1109/mgrs.2023.3329928","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3329928","url":null,"abstract":"Today, remote sensing systems/technologies are one of the most essential requirements for civil and military sectors for various applications. This review article discusses the evolutionary developments of today’s remote sensing radar/optical/electronic warfare (EW) technologies, right from the telemobiloscope. This review article addresses the fundamentals of radar sensing techniques, top-level radar classifications, and revolutionary developments of antenna technologies for remote sensing applications. The various techniques available for radar waveform design, a characteristics analysis of it using ambiguity functions (AFs), pulse compression/stretch processing, a time-frequency (T-F) analysis, and so on are reviewed. The significant transformations that have happened in radar system engineering since vacuum tube microwave devices are reported. Contemporary societal applications of radar systems, tracking and guidance radar systems, advanced EW systems, photonics EW systems, and photonics signal processing are reviewed and reported. State-of-the-art optical technologies available for today’s remote sensing applications are discussed. In addition to these reviews, a comprehensive comparative study is performed in terms of available remote sensing systems/technologies, their typical operating frequency ranges, potential applications, types of waveforms, and so forth, and the quantitative results are reported.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"54 1","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071459","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}

{"title":"There Are No Data Like More Data: Datasets for deep learning in Earth observation","authors":"Michael Schmitt, S. A. Ahmadi, Yonghao Xu, G. Taşkın, Ujjwal Verma, F. Sica, R. Hänsch","doi":"10.1109/MGRS.2023.3293459","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3293459","url":null,"abstract":"Carefully curated and annotated datasets are the foundation of machine learning (ML), with particularly data-hungry deep neural networks forming the core of what is often called artificial intelligence (AI). Due to the massive success of deep learning (DL) applied to Earth observation (EO) problems, the focus of the community has been largely on the development of evermore sophisticated deep neural network architectures and training strategies. For that purpose, numerous task-specific datasets have been created that were largely ignored by previously published review articles on AI for EO. With this article, we want to change the perspective and put ML datasets dedicated to EO data and applications into the spotlight. Based on a review of historical developments, currently available resources are described and a perspective for future developments is formed. We hope to contribute to an understanding that the nature of our data is what distinguishes the EO community from many other communities that apply DL techniques to image data, and that a detailed understanding of EO data peculiarities is among the core competencies of our discipline.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"63-97"},"PeriodicalIF":14.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44018081","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}

{"title":"Interferometric Phase Linking: Algorithm, application, and perspective","authors":"Dinh Ho Tong Minh, Stefano Tebaldini","doi":"10.1109/mgrs.2023.3300974","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3300974","url":null,"abstract":"Mitigating decorrelation effects on interferometric synthetic aperture radar (InSAR) time series data is challenging. The phase linking (PL) algorithm has been the key to handling signal decorrelations in the past 15 years. Numerous studies have been carried out to enhance its precision and computational efficiency. Different PL algorithms have been proposed, each with unique phase optimization approaches, such as the quasi-Newton method, equal-weighted and coherence-weighted factors, component extraction and selection SAR (CAESAR), and eigendecomposition-based algorithm (EMI). The differences among the PL algorithms can be attributed to the weight criteria adopted in each algorithm, which can be coherence-based, sparsity-based, or other forms of regularization. The PL algorithm has multiple applications, including SAR tomography (TomoSAR), enhancing distributed scatterers (DSs) to combine with persistent scatterers (PS) in PS and DS (PSDS) techniques, and compressed PSDS InSAR (ComSAR), where it facilitates the retrieval of the optimal phase from all possible measurements. This article aims to review PL techniques developed in the past 15 years. The review also underscores the importance of the PL technique in various SAR applications (TomoSAR, PSDS, and ComSAR). Finally, the deep learning (DL) approach is discussed as a valuable tool to improve the accuracy and efficiency of the PL process.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134915065","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}

{"title":"Letter From the President [President’s Message]","authors":"Mariko Burgin","doi":"10.1109/mgrs.2023.3304090","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3304090","url":null,"abstract":"Hello again! My name is Mariko Burgin, and I am the IEEE Geoscience and Remote Sensing Society (GRSS) president. You can reach me at president@grss-ieee.org and @GRSS_President on X, formally known as Twitter.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134917545","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}

{"title":"The Orbital X-Band Real-Aperture Side-Looking Radar of Cosmos-1500: A Ukrainian IEEE Milestone candidate","authors":"Ganna B. Veselovska-Maiboroda, Sergey A. Velichko, A. Nosich","doi":"10.1109/MGRS.2023.3294708","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3294708","url":null,"abstract":"We revisit the development and operation of the orbital X-band real-aperture side-looking radar (RA-SLR) onboard the USSR satellite Cosmos-1500 in the historical context. This radar was conceived, designed, and tested in the early 1980s and then supervised, in orbit, by a team of Ukrainian scientists and engineers led by Prof. Anatoly I. Kalmykov (1936–1996) at the O. Y. Usikov Institute of Radiophysics and Electronics (IRE) of the National Academy of Sciences of Ukraine (NASU). It had a magnetron source, a 12-m deployable slotted-waveguide antenna, and an onboard signal processing unit. Instead of preplanned meticulous experiments, only five days after placement into the polar Earth orbit in the autumn of 1983, the SLR of Cosmos-1500 rendered truly outstanding service. It provided a stream of microwave images of the polar sea ice conditions that enabled the rescue of freighters in the Arctic Ocean. Two years later, similar imagery was equally important in the rescue of a motor vessel (MV) in the Antarctic. However, the way to success was far from smooth. Besides the technical problems, Kalmykov had to overcome the jealousy and hostility of his home institute administration, colleagues from Moscow research laboratories, and high-level USSR bureaucracy. Later, Kalmykov’s radar was released to the industry and became the main instrument of the USSR and Russian series of remote sensing satellites Okean and Ukrainian satellites Sich-1 and Sich-1M. We believe that the RA-SLR of Cosmos-1500 is a good candidate for the status of an IEEE Milestone in Ukraine.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"8-20"},"PeriodicalIF":14.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47447139","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}

{"title":"Airborne Lidar Data Artifacts: What we know thus far","authors":"Wai Yeung Yan","doi":"10.1109/MGRS.2023.3285261","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3285261","url":null,"abstract":"Data artifacts are a common occurrence in airborne lidar point clouds and their derivatives [e.g., intensity images and digital elevation models (DEMs)]. Defects, such as voids, holes, gaps, speckles, noise, and stripes, not only degrade lidar visual quality but also compromise subsequent data-driven analyses. Despite significant progress in understanding these defects, end users of lidar data confronted with artifacts are stymied by the scarcities of both resources for the dissemination of topical advances and analytic software tools. The situation is exacerbated by the wide-ranging array of potential internal and external factors, with examples including weather/atmospheric/Earth surface conditions, system settings, and laser receiver–transmitter axial alignment, that underlie most data artifact issues. In this article, we provide a unified overview of artifacts commonly found in airborne lidar point clouds and their derivatives and survey the existing literature for solutions to resolve these issues. The presentation is from an end-user perspective to facilitate rapid diagnoses of issues and efficient referrals to more specialized resources during data collection and processing stages. We hope that the article can also serve to promote coalescence of the scientific community, software developers, and system manufacturers for the ongoing development of a comprehensive airborne lidar point cloud processing bundle. Achieving this goal would further empower end users and move the field forward.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"21-45"},"PeriodicalIF":14.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44296741","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}