Decision-Level Image Fusion
Image fusion can be performed at three levels: pixel level, feature level, and decision level. Among them, decision-level fusion is a high-level information fusion, which is less explored and is a hot spot in the field of information fusion. Compared to l
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Image Fusion
Image Fusion
Gang Xiao • Durga Prasad Bavirisetti • Gang Liu • Xingchen Zhang
Image Fusion
Gang Xiao School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai, China
Durga Prasad Bavirisetti School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai, China
Gang Liu School of Automation Engineering Shanghai University of Electrical Power Shanghai, China
Xingchen Zhang School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai, China
ISBN 978-981-15-4866-6 ISBN 978-981-15-4867-3 https://doi.org/10.1007/978-981-15-4867-3
(eBook)
Jointly published with Shanghai Jiao Tong University Press, Shanghai, China The print edition is not for sale in China Mainland. Customers from China Mainland please order the print book from: Shanghai Jiao Tong University Press. © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press 2020 This work is subject to copyright. All rights are reserved 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 any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The 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. The 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. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
This book is dedicated to the memory of my father. He was away from us during my PhD study
Preface
Image fusion has been a hot topic for many years, and it has been widely applied to fields varying from robotics, medical engineering, and surveillance to military and many others. However, almost 10 years have passed since the publication of the last book on image fusion. Within these years, we have seen a rapid progress and more applications of image fusion, which have not been reviewed systematically. For instance, with the emergence and fast growing of artificial intelligence, some researchers have begun to investigate how machine learning and deep learning would benefit image fusion. Therefore, we think the
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