[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116257-en":3,"doc-seo-116257-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116257,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Computational Intelligence for Remote Sensing Image Change Detection","Remote sensing technology enables non-contact, large-scale observation of Earth by analyzing reflected or emitted electromagnetic radiation across spectral bands, supporting disaster management, sustainability, ecological conservation, and national security. As a key remote sensing interpretation task, remote sensing image change detection analyzes multi-temporal images to reveal temporal evolution patterns in land cover for dynamic monitoring, post-disaster assessment, and strategic surveillance. The book reviews limitations of manual-feature and statistical approaches and explains how computational intelligence, especially deep learning and neural architecture search, improves change detection performance under noise, radiometric inconsistencies, and heterogeneous scenes.","SpringerBriefs in Computer Science  \nSpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic.  \nTypical topics might include:  \n A timely report of state-of-the art analytical techniques  \n A bridge between new research results, as published in journal articles, and a contextual literature review  \n A snapshot of a hot or emerging topic  \n An in-depth case study or clinical example  \n A presentation of core concepts that students must understand in order to make independent contributions.  \nBriefs allow authors to present their ideas and readers to absorb them with minimal time investment. Briefs will be published as part of Springer ’seBook collection, with millions of users worldwide. In addition, Briefs will be available for individual print and electronic purchase. Briefs are characterized by fast, global electronic dissemination, standard publishing contracts, easy-to-use manuscript preparation and formatting guidelines, and expedited production schedules. We aim for publication 8–12 weeks after acceptance. Both solicited and unsolicited manuscripts are considered for publication in this series.  \n**Indexing: This series is indexed in Scopus, Ei-Compendex, and zbMATH **  \n[OceanofPDF.com](OceanofPDF.com)  \nJiao Shi, Yu Lei, Maoguo Gong and Nan Zhang  \nComputational Intelligence for Remote Sensing Image Change Detection  \n[OceanofPDF.com](OceanofPDF.com)  \nJiao Shi  \nSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China  \nYu Lei  \nSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China  \nMaoguo Gong  \nCollege of Mathematics Science, Inner Mongolia Normal University, Hohhot, NeiMongol, China  \nNan Zhang  \nSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China  \nISSN 2191-5768 e-ISSN 2191-5776  \nSpringerBriefs in Computer Science  \nISBN 978-981-92-1403-7 e-ISBN 978-981-92-1404-4  \n[https://doi.org/10.1007/978-981-92-1404-4](https://doi.org/10.1007/978-981-92-1404-4)  \n© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026  \nThis work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in anyother physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.  \nThe use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nThe publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nThis Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.  \nThe registered company address is: 152 Beach Road, \\#21-01/04 Gateway East, Singapore 189721, Singapore  \n[OceanofPDF.com](OceanofPDF.com)  \nPreface  \nRemote sensing technology enables non-contact, large-scale observation of the Earth’s surface by measuring the reflected or emitted electromagnetic radia","cbCaigFoyBgveoUy","https://ap.wps.com/l/cbCaigFoyBgveoUy","pdf",42999146,1,196,"English","en",105,"# Preface\n## Remote sensing and change detection\n## Limitations of traditional methods\n## Computational intelligence and deep learning approaches","[{\"question\":\"What problem does remote sensing image change detection address?\",\"answer\":\"It analyzes multi-temporal remote sensing images to automatically identify temporal evolution patterns in land cover, supporting environmental monitoring, post-disaster assessment, and surveillance.\"},{\"question\":\"Why do traditional change detection methods often perform poorly?\",\"answer\":\"They rely on manual features and statistical modeling, which can suffer from error propagation, limited representational capacity, and weak generalization under diverse land cover, radiometric inconsistencies, and noise interference.\"},{\"question\":\"How does computational intelligence improve change detection?\",\"answer\":\"Computational intelligence—especially deep learning—learns hierarchical feature representations from raw data and can capture complex spatio-temporal dynamics, while neural architecture search further enables efficient, task-adapted network design.\"}]","Computational Intelligence for Remote Sensing Image Change Detection | 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