Proceedings of the IEEE最新文献

{"title":"Hybrid Deep Learning Models for Remote Sensing Image Processing","authors":"Matthieu Muller;Daniele Picone;Begüm Demir;Gustau Camps-Valls;Mauro Dalla Mura;Magnús Örn Úlfarsson;Jón Atli Benediktsson","doi":"10.1109/JPROC.2025.3638871","DOIUrl":"10.1109/JPROC.2025.3638871","url":null,"abstract":"Core image processing tasks, such as super-resolution, denoising, deblurring, pansharpening, and atmospheric correction, underpin all optical remote sensing (RS) pipelines. Errors at this stage propagate through downstream applications, distorting land-cover maps, change detection, and climate records. Classical physics-based models capture sensor optics, radiometry, and geometry but struggle with complex noise and scene variability. In contrast, deep learning (DL) methods offer powerful data-driven solutions yet often act as closed boxes, ignoring physical constraints and overfitting to spurious patterns. Hybrid DL (HDL) approaches bridge this gap by integrating physical models with neural architectures, combining interpretability and data adaptivity. This article surveys the emerging landscape of HDL methods in RS image processing, outlining their theoretical foundations, motivations, and design philosophies. We categorize fusion strategies, from model-embedded schemes (e.g., plug-and-play (PnP) and unrolling) to model-guided learning (e.g., deep image prior (DIP) and unsupervised frameworks), and discuss how they enhance trust, robustness, and physical consistency in RS image analysis.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"396-416"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Polar Radiant Energy in the Far-Infrared Experiment (PREFIRE), Broadband Thermal Spectrometry for Small Satellite Platforms","authors":"Brian J. Drouin;Marc C. Foote;Chad A. Greene;Brian H. Kahn;Sharmila Padmanabhan;Mary White;Xianglei Huang;Xiuhong Chen;Aronne Merrelli;Hazem Mahmoud;Kyle Mattingly;Timothy Michaels;Nathaniel B. Miller;Hamish Prince;Erin Wagner Hokanson;Natasha Vos;Tristan L’Ecuyer","doi":"10.1109/JPROC.2026.3667066","DOIUrl":"10.1109/JPROC.2026.3667066","url":null,"abstract":"For more than a decade, the remote sensing community has called for longwave spectral measurements of the Earth system to facilitate closing the radiation budget. In an effort to address this large gap in Earth-observing capability, NASA has supported the development and implementation of the Polar Radiant Energy in the Far-Infrared (FIR) Experiment (PREFIRE), which realizes two miniaturized thermal IR spectrometers (TIRSs), one on each of two polar orbiting small satellites with different ascending node crossing times. The spectral radiances measured by the TIRS instruments are utilized to extract cloud presence, atmospheric state, surface properties, and the associated top-of-atmosphere (TOA) spectral fluxes at both poles. This article describes the major system and algorithm components and introduces the geophysical products. PREFIRE data will be used to inform and improve ice-sheet and coupled-Earth system models to better predict the future state of the Earth’s poles.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"345-361"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11427316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2026.3700758","DOIUrl":"10.1109/JPROC.2026.3700758","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"417-417"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11613158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Foundation","authors":"","doi":"10.1109/JPROC.2026.3707916","DOIUrl":"10.1109/JPROC.2026.3707916","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"418-418"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11613159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spaceborne GNSS-R Bistatic Radar Remote Sensing, CYGNSS, and Future Missions","authors":"Christopher Ruf;Scott Gleason","doi":"10.1109/JPROC.2025.3583997","DOIUrl":"10.1109/JPROC.2025.3583997","url":null,"abstract":"Global Navigation Satellite System Reflectometry (GNSS-R) is a relatively new type of radar developed for remote sensing of the Earth surface. It uses GNSS navigation signals such as those transmitted by the Global Positioning System (GPS) constellation of satellites as the radar transmitter. The radar receiver measures alterations in the navigation signal caused by scattering from the Earth surface. The nature of those alterations contains information about the surface, which can be retrieved. The general theory of GNSS-R remote sensing and its historical development is presented, followed by a detailed assessment of the performance and capabilities of the first dedicated spaceborne GNSS-R science mission, NASA’s Cyclone Global Navigation Satellite System (CYGNSS). Numerous follow-on GNSS-R missions are also noted that have either already been launched or are scheduled to be soon. A wide range of science data products and scientific applications of the CYGNSS data are examined. Developments that are currently underway in GNSS-R technology are summarized, and their potential impact on the capabilities of future GNSS-R missions is discussed.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"308-326"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BIOMASS: ESA’s P-Band SAR Mission","authors":"Klaus Scipal;Clément Albinet;Michele Caccia;Adriano Carbone;Nuno Carvalhais;Jérôme Chave;Jørgen Dall;Michael Fehringer;Antonio Leanza;Thuy Le Toan;Maktar Malik;Antonio Novelli;Philippe Paillou;Kostas P. Papathanassiou;Janice Patterson;Muriel Pinheiro;Shaun Quegan;Markus Reichstein;Björn Rommen;Sassan Saatchi;Herman H. Shugart;Tristan Simon;Stefano Tebaldini;Lars M. H. Ulander;Antonio Valentino;Philip Willemsen;Mathew Williams","doi":"10.1109/JPROC.2026.3687416","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3687416","url":null,"abstract":"The European Space Agency’s (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission’s primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite’s advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"220-235"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on Earth Remote Sensing and Data Processing","authors":"Leung Tsang;Joel T. Johnson;Jiancheng Shi;Irena Hajnsek;Jeff Dozier","doi":"10.1109/JPROC.2026.3700784","DOIUrl":"10.1109/JPROC.2026.3700784","url":null,"abstract":"The articles in this special issue span the areas of both active and passive microwave remote sensing of the Earth (including the use of signals-ofopportunity), optical sensing, and the use of AI/ML methods.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"179-182"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11613157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Revisit-Rate Tropical Cyclone Observations From the NASA TROPICS Satellite Constellation Mission","authors":"William J. Blackwell;Scott A. Braun;George R. Alvey;Robert Atlas;Ralf Bennartz;Jessica Braun;Kerri Cahoy;Ruiyao Chen;Galina Chirokova;Brittany Dahl;James Darlow;Mark DeMaria;Michael Diliberto;Jason P. Dunion;Patrick Duran;Thomas J. Greenwald;Sarah Griffin;Zachary Griffith;Derrick Herndon;Jeffrey D. Hawkins;Satya Kalluri;C. Kidd;Min-Jeong Kim;R. Vincent Leslie;Frank Marks;Toshi Matsui;W. McCarty;Adam Milstein;Glenn Perras;Michael L. Pieper;Robert Rogers;Christopher Velden;Yalei You;Nick V. Zorn","doi":"10.1109/JPROC.2025.3582502","DOIUrl":"10.1109/JPROC.2025.3582502","url":null,"abstract":"New satellite constellations to provide high-resolution atmospheric observations from microwave (MW) sounders operating in low-Earth orbit are now coming online and are providing operationally useful data. The first of these missions, the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 7 and 25, 2023 (Eastern Daylight Time, two CubeSats in each of the two launches). TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS is providing rapid-refresh MW measurements (median refresh rate of better than 60 min early in the mission with four functional CubeSats, and now approximately 70–90 min with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of TCs have been captured thus far by the TROPICS mission, revealing the detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing TC analysis at operational centers. Here, we present an overview of the TROPICS mission after two years of successful science operations with a focus on the suite of geophysical (Level 2) products (atmospheric vertical temperature and moisture profiles, instantaneous surface rain rate, and TC intensity) and the science investigations that have been enabled by these new measurements.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"236-260"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11103576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDGSAT-1: A Professional Scientific Satellite for Monitoring SDG Indicators","authors":"Huadong Guo;Changyong Dou;Dong Liang;Nijun Jiang;Bihong Fu;Chengshan Han;Fansheng Chen;Peng Huang;Juanjuan Jing;Yu Zhang;Bo Cheng;Xiaoxue Feng;Yunwei Tang;Yonghong Hu;Lin Yan;Hao Zhang","doi":"10.1109/JPROC.2025.3649854","DOIUrl":"10.1109/JPROC.2025.3649854","url":null,"abstract":"The implementation of the United Nations (UN) 2030 Agenda for Sustainable Development (2030 Agenda), with its 17 sustainable development goals (SDGs), faces challenges such as insufficient data, limited research methodologies, and uneven progress across regions. Earth observation (EO), particularly scientific satellites, offers unique advantages in supporting global sustainable development by providing objective, dynamic, and large-scale datasets for SDG evaluations and policymaking, as well as by facilitating the study of Earth’s environmental systems and their interactions with human activity. Sustainable Development Science Satellite 1 (SDGSAT-1), the world’s first scientific satellite dedicated to supporting the 2030 Agenda, was designed and developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS). Its three advanced EO sensors, i.e., Thermal Infrared Spectrometer (TIS), Glimmer Imager (GLI), and Multispectral Imager (MSI), furnish high-quality data, enabling continuous monitoring of human activity and environmental changes to bolster SDG-related research and global sustainability initiatives. As of November 2025, SDGSAT-1 has collected over 480 000 global terrain coverage images since its launch in November 2021. All its datasets have been shared free of charge with the Global Scientific Community through the SDGSAT-1 Open Science Program initiated in September 2022. The datasets have enabled researchers from more than 110 countries, 10 UN agencies, and various international organizations to publish over 180 scientific articles, 17 UN reports, and numerous public data products. These have demonstrated applications in urban development, disaster response, environmental monitoring, agriculture, and marine conservation. This article reviews the technical innovations and mission specifications of the SDGSAT-1 satellite, demonstrates its contribution in leveraging space technology for SDG monitoring and evaluation, and discusses the future evolution of development of EO systems, specifically the planned Sustainable Development Satellite Constellation, for supporting the global achievement of SDGs.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"327-344"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Women in Engineering Membership Benefits","authors":"","doi":"10.1109/JPROC.2026.3707918","DOIUrl":"10.1109/JPROC.2026.3707918","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 2","pages":"420-420"},"PeriodicalIF":30.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11613559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}