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Letter From the President [President’s Message] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-07 Mariko Burgin
Welcome to the IEEE Geoscience and Remote Sensing Society (GRSS) in 2024! I am delighted to serve one more year as the GRSS president and hope we can accomplish great things together. I welcome your thoughts at president@grss-ieee.org, so don’t hesitate to reach out, even just to say hello.
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IEEE GRSS Bombay Chapter Activities [Chapters] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Gulab Singh, Baljeet Singh Cheema, Komal Rai
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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IDEA Day at IGARSS 2023: Advancing diversity and inclusion in the GRSS [Idea Committee] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Paula C. B. V. Santos, Margot Flemming, Qian Song, Victoria Vanthof
On 17–21 July 2023, more than 3,000 engineers, geoscientists, and remote sensing enthusiasts from around the world congregated in Pasadena, California, for the 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). The Inspire, Develop, Empower, and Advance (IDEA) Committee led a series of impactful events throughout the week-long conference, centering on themes of driving transformation
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Tech RXIV: Share Your Preprint Research With the World! IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06
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Empowerment in Progress: Celebrating the Growth of GRSS’ Women Mentoring Women Program [Idea Committee] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Paula Castro Brandão Vaz Dos Santos, Margot Flemming, Vicky Vanthof
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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High Performance and Disruptive Computing in Remote Sensing: The Third Edition of the School Organized by the HDCRS Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Gabriele Cavallaro, Dora Blanco Heras, Manil Maskey
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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International Coordination for Spaceborne Synthetic Aperture Radar: A Personal Impression [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Marwan Younis
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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SAR Image Analysis—A Computational Statistics Approach: With R Code, Data, and Applications [Book Reviews] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Fernando A. Peña-Ramírez
The book SAR Image Analysis—A Computational Statistics Approach: With R Code, Data, and Applications stands out as an exceptional resource dedicated to statistical methodologies to extract information from synthetic aperture radar (SAR) imagery, all within a computational framework using R programming language. The book covers a wide range of topics in 183 pages and seven chapters, including a detailed
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Moving Forward [From the Editor] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Paolo Gamba
Let me start this editorial by announcing that I am stepping down from my position of editor-in-chief (EIC) of IEEE Geoscience and Remote Sensing Magazine ( GRSM ) soon. I will provide a short summary of my term in the “From the Editor” column in the next (and my last) GRSM issue. Today, I simply want to highlight that the search for a new EIC is underway, and all the details are available on the webpage
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International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing 2023: A Brief Report [Conference Reports] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Thogarcheti Hitendra Sarma, Alejandro C. Frery, Avik Bhattacharya, Nidamanuri Rama Rao, B. S. Daya Sagar
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Computer Vision for Earth Observation: The Second GRSS Image Analysis and Data Fusion School [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Silvia Liberata Ullo, Gemine Vivone, Gülşen Taşkın, Ronny Hänsch, Ujjwal Verma
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Airborne Sounding Radar for Desert Subsurface Exploration of Aquifers: Desert-SEA: Mission concept study [Space Agencies] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-03-06 Essam Heggy, Mahta Moghaddam, Elizabeth M. Palmer, William M. Brown, J. Lee Blanton, Mikołaj Kosinski, Paul Sirri, Edgar A. Dixon, Abotalib Z. Abotalib, Jonathan C. L. Normand, John Clark, Gary Klemens, Matthieu Agranier, François Guillon, Akram A. Abdellatif, Tamer Khattab, Zlatan Tsvetanov, Mohamed Shokry, Noor Al-Mulla, Mohamed Ramah, Sayed M. Bateni, Alireza Tabatabaeenejad, Jean-Philippe Avouac
Shallow aquifers are the largest freshwater bodies in the North African Sahara and the Arabian Peninsula. Their groundwater dynamics and response to climatic variability and anthropogenic discharge remain largely unquantified due to the absence of large-scale monitoring methods. Currently, the assessment of groundwater dynamics in these aquifer systems is made primarily from sporadic well logs that
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The Synergy Between Remote Sensing and Social Sensing in Urban Studies: Review and perspectives IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-01-04 Xiaoyue Xing, Bailang Yu, Chaogui Kang, Bo Huang, Jianya Gong, Yu Liu
Urban studies require a rich set of information sources and techniques that enable a comprehensive depiction of urban environments. Remote sensing captures physical characteristics of urban landscapes, while social sensing collects data from social media and digital devices to reflect human activities. The combination of remote sensing and social sensing has been employed to investigate urban environments
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Interferometric Synthetic Aperture Radar Statistical Inference in Deformation Measurement and Geophysical Inversion: A review IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2024-01-03 Chisheng Wang, Ling Chang, Xiang-Sheng Wang, Bochen Zhang, Alfred Stein
With the rapid advancements in synthetic aperture radar (SAR) satellites and associated processing algorithms over recent decades, interferometric SAR (InSAR) has emerged as a routine method for monitoring large-scale ground deformation and interpreting geophysical processes. Statistical inference serves as a major component in InSAR technique developments and applications. This article provides an
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DeepBlue: Advanced convolutional neural network applications for ocean remote sensing IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-28 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
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Why Does the GRSS Need a Magazine? [From the Editor] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 Paolo Gamba
Since you are reading this editorial, you are well aware that this journal is quite different than the other ones published by the IEEE Geoscience and Remote Sensing Society (GRSS). Indeed, it is a magazine, which implies that it has some specific features. In the past two issues I introduced how the technical contents of IEEE Geoscience and Remote Sensing Magazine ( GRSM ) are selected and how authors
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Letter From the President [President’s Message] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 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).
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Report on the 2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 Ronny H채nsch, Claudio Persello, Gemine Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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The Instrumentation and Future Technology Technical Committee’s Second “Summer School”: Auckland, New Zealand [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 Delwyn Moller, Catherine Qualtrough, Scott Gleason, Scott Hensley, Mahta Moghaddam, Andrew O’Brien, Brian Pollard, Wolfgang Rack, Chris Ruf, Michelangelo Villano
The Instrumentation and Future Technologies Technical Committee’s (IFT-TC) second summer school took place in Auckland, New Zealand, from 30 January to 3 February 2023. The IFT Remote Sensing Summer Schools (IFT-R3S) aim to promote future research in remote sensing; connect Master students, junior Ph.D. students, recent graduates, and young professionals with research groups linked to the IFT-TC; and
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Erratum IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 R. Thoreau
Presents corrections to the paper, “Active learning for hyperspectral image classification: A comparative review”.
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The Second International Soil Moisture School [Conference Reports] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 L. Karthikeyan, Avik Bhattacharya, Jasmeet Judge, Simon Yueh
The Second International Soil Moisture School (ISMS2023) was conducted at the Indian Institute of Technology Bombay (IITB), India, during 15–17 March 2023, after the success of the first school held at the University of Massachusetts, Amherst, MA, USA. ISMSs aim to provide early career scientists with an understanding and utilization of remotely sensed soil moisture (SM) information from current and
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The IEEE Geoscience and Remote Sensing Society “Open PocketQube Kit”: An affordable open source approach to Earth observation missions [Education in Remote Sensing] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 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
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Open Source Data Programs From Low-Earth Orbit Synthetic Aperture Radar Companies: Questions and answers [Industry Profiles and Activities] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-12-21 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).
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Polarimetric Roll-Invariant Features and Applications for Polarimetric Synthetic Aperture Radar Ship Detection: A comprehensive summary and investigation IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-11-29 Si-Wei Chen, Ming-Dian Li, Xing-Chao Cui, Hao-Liang Li
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),
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Evolutionary Developments of Today’s Remote Sensing Radar Technology—Right From the Telemobiloscope: A review IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-11-21 Samedh Sachin Kari, A Arockia Bazil Raj, Balasubramanian. K
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
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Remote Sensing Object Detection Meets Deep Learning: A metareview of challenges and advances IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-10-24 Xiangrong Zhang, Tianyang Zhang, Guanchun Wang, Peng Zhu, Xu Tang, Xiuping Jia, Licheng Jiao
Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received long-standing attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this article aims to present a comprehensive
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Real-Time Semantic Segmentation: A brief survey and comparative study in remote sensing IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-10-24 Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya
Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. It has many applications, including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. With the success of efficient deep learning methods [i.e., efficient deep neural networks (DNNs)] for real-time semantic
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Invertible Physics-Based Hyperspectral Signature Models: A review IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-10-11 Marianne Al Hayek, Catherine Baskiotis, Josselin Aval, Marwa Elbouz, Bachar El Hassan
The richness of hyperspectral imaging (HSI)-collected signals makes possible quantitative inference by the inverse problem-solving of the chemical and biophysical parameters of the imaged object. In this article, we first propose a classification of the large variety of literature on invertible physics-based hyperspectral signature models by analyzing their founding hypotheses and methodologies. All
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Language Integration in Remote Sensing: Tasks, datasets, and future directions IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-10-11 Laila Bashmal, Yakoub Bazi, Farid Melgani, Mohamad M. Al Rahhal, Mansour Abdulaziz Al Zuair
The emerging field of vision–language models, which combines computer vision and natural language processing (NLP), has gained significant interest and exploration. This integration has opened up new research opportunities, particularly in remote sensing (RS), where it has the potential to enhance RS systems’ capabilities. In this context, this article presents a comprehensive review of more than 100
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Topographic Correction of Optical Remote Sensing Images in Mountainous Areas: A systematic review IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-27 Rui Chen, Gaofei Yin, Wei Zhao, Kai Yan, Shengbiao Wu, Dalei Hao, Guoxiang Liu
Rugged terrain distorts optical remote sensing observations and subsequently impacts land cover classification and biophysical and biochemical parameter retrieval over mountainous areas. Therefore, topographic correction (TC) is a prerequisite for many remote sensing applications. Although various TC methods have been explored over the past four decades to mitigate topographic effects, a systematic
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Letter From the President [President’s Message] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-26 Mariko Burgin
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.
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CaBuAr: California burned areas dataset for delineation [Software and Data Sets] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Daniele Rege Cambrin, Luca Colomba, Paolo Garza
Forest wildfires represent one of the catastrophic events that, over the last decades, have caused huge environmental and humanitarian damage. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools
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Reinforcing Our Commitment: Why DEI Matters for the IEEE GRSS [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Victoria Vanthof, Heather McNairn, Stephanie Tumampos, Mariko Burgin
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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IGARSS 2023 in Pasadena, California: Impressions of the First Days [Conference Reports] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Alberto Moreira, Francesca Bovolo, David Long, Antonio Plaza
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Issues and Special Issues [From the Editor] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Paolo Gamba
In line with what I did for the June issue, I will use my IEEE Geoscience and Remote Sensing Magazine ( GRSM ) editorial on one hand to summarize the content of the current issue and, on the other hand, to introduce a feature of this magazine that may not be well known to (or understood by) all our readers. Specifically, I will describe the possibility to publish special issues in GRSM . As mentioned
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Interferometric Phase Linking: Algorithm, application, and perspective IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Dinh Ho Tong Minh, Stefano Tebaldini
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
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SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, Xiao Xiang Zhu
Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset Self-Supervised Learning for Earth Observation-Sentinel-1/2 ( SSL4EO - S12
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A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-09-25 Manil Maskey, Gabriele Cavallaro, Dora Blanco Heras, Paolo Fraccaro, Blair Edwards, Iksha Gurung, Brian Freitag, Muthukumaran Ramasubramanian, Johannes Jakubik, Linsong Chu, Raghu Ganti, Rahul Ramachandran, Kommy Weldemariam, Sujit Roy, Carlos Costa, Alex Corvin, Anish Asthana
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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There Are No Data Like More Data: Datasets for deep learning in Earth observation IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-08-09 Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu, Gülşen Taşkin, Ujjwal Verma, Francescopaolo Sica, Ronny Hänsch
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
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The Orbital X-Band Real-Aperture Side-Looking Radar of Cosmos-1500: A Ukrainian IEEE Milestone candidate IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-25 Ganna B. Veselovska-Maiboroda, Sergey A. Velichko, Alexander I. Nosich
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
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Why Does GRSM Require the Submission of White Papers? [From the Editor] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Paolo Gamba
As you have already guessed from the title, and in line with my editorial in the March 2023 issue, I will use my space here to address two different points. First, the reader will find a summary of the contents of this issue, which is useful to those who would like to quickly navigate the issue and read only what they are interested in. The second part of this editorial will be devoted instead to better
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Letter From the President [President’s Message] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-13 Mariko Burgin
Hello and nice to see you again! My name is Mariko Burgin, and I am the IEEE Geoscience and Remote Sensing Society (GRSS) President. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.
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Taking Artificial Intelligence Into Space Through Objective Selection of Hyperspectral Earth Observation Applications: To bring the “brain” close to the “eyes” of satellite missions IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Agata M. Wijata, Michel-François Foulon, Yves Bobichon, Raffaele Vitulli, Marco Celesti, Roberto Camarero, Gianluigi Di Cosimo, Ferran Gascon, Nicolas Longépé, Jens Nieke, Michal Gumiela, Jakub Nalepa
Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned into useful information onboard a satellite; hence
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Onboard Information Fusion for Multisatellite Collaborative Observation: Summary, challenges, and perspectives IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Gui Gao, Libo Yao, Wenfeng Li, Linlin Zhang, Maolin Zhang
Onboard information fusion for multisatellites, which is based on spatial computing mode, can improve the satellites’ capability, such as the spatial–temporal coverage, detection accuracy, recognition confidence, position precision, and prediction precision for disaster monitoring, maritime surveillance, and other emergent or continuous persistent observing situations. First, we analyze the necessity
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AI Security for Geoscience and Remote Sensing: Challenges and future trends IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Yonghao Xu, Tao Bai, Weikang Yu, Shizhen Chang, Peter M. Atkinson, Pedram Ghamisi
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising
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Generative Artificial Intelligence and Remote Sensing: A perspective on the past and the future [Perspectives] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Nirav Patel
The first phase of 2023 has been marked with an explosion of interest around generative AI systems, which generate content. This type of machine learning promises to enable the creation of synthetic data and outputs in many different modalities. OpenAI’s ChatGPT has certainly taken the world by storm and opened discourse on how the technology should be used.
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Analysis-Ready Data and FAIR-AI—Standardization of Research Collaboration and Transparency Across Earth-Observation Communities [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Dalton Lunga, Silvia Ullo, Ujjwal Verma, George Percivall, Fabio Pacifici, Ronny Hänsch
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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REACT: A New Technical Committee for Earth Observation and Sustainable Development Goals [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Irena Hajnsek, Subit Chakrabarti, Andrea Donnellan, Rabia Munsaf Khan, Carlos López-Martínez, Ryo Natsuaki, Anthony Milne, Avik Bhattacharya, Praveen Pankajakshan, Pooja Shah, Muhammad Adnan Siddique
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Computer Vision for Earth Observation—The First IEEE GRSS Image Analysis and Data Fusion School [Technical Committees] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-12 Gemine Vivone, Dalton Lunga, Francescopaolo Sica, Gülşen Taşkin, Ujjwal Verma, Ronny Hänsch
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Airborne Lidar Data Artifacts: What we know thus far IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-07-04 Wai Yeung Yan
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
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Not the Usual Editorial [From the Editor] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-03-31 Paolo Gamba
Readers of this magazine know that the IEEE Geoscience and Remote Sensing Magazine ( GRSM ) editorial usually summarizes the content of the issue, providing hints to the interested researchers and practitioners to make it easier to find the articles or topics they are looking for. Since this is my first editorial, however, I will ask for your patience because I would like to introduce myself and a
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Letter From the President [President’s Message] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-04-03 Mariko Burgin
Hello and nice to meet you! My name is Mariko Burgin and I am the incoming IEEE Geoscience and Remote Sensing Society (GRSS) president. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.
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The IEEE GRSS IDEA Committee: Championing Diversity in Adversity [Women in GRSS] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-03-31 Stephanie Tumampos, Shawn C. Kefauver
After more than two years of hard lockdowns and restricted mobility, the whole world carefully returns to normalcy. With this, the IEEE Geoscience and Remote Sensing Society (GRSS) Inspire, Develop, Empower, and Advance (IDEA) Committee took the opportunity to attend two conferences in person, continuing its campaign for inclusivity and diversity.
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MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets] IEEE Geosci. Remote Sens. Mag. (IF 16.2) Pub Date : 2023-03-31 Burak Ekim, Timo T. Stomberg, Ribana Roscher, Michael Schmitt
The advancement in deep learning (DL) techniques has led to a notable increase in the number and size of annotated datasets in a variety of domains, with remote sensing (RS) being no exception [1] . Also, an increase in Earth observation (EO) missions and the easy access to globally available and free geodata have opened up new research opportunities. Although numerous RS datasets have been published