Synthetic Aperture Radar Target Recognition under Limited Training Data - Theory and Methods

Aug 7, 2026

Focused on synthetic aperture radar (SAR) automatic target recognition when labeled data is scarce, the work frames limited training as a fundamental challenge for modeling, inference, and explanation rather than a mere fitting problem. It develops a theory-driven framework that distinguishes intrinsic causal features from spurious correlations to clarify why recognition fails in data-scarce environments and how performance can be improved. The approach integrates SAR imaging mechanisms with computational learning theory, bridging robust causal modeling and practical SAR applications.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages201

Reading Time8 h 27 min

AccessFree to download

Foreword

Background and motivation

Causal inference–driven framework

Technical value and intended audience

Acknowledgments

Support and guidance during doctoral study

Mentors and colleagues’ contributions

Chenwei Wang, Jifang Pei and Yulin Huang Synthetic Aperture Radar Target Recognition under Limited Training Data Theory and Methods [OceanofPDF.com](OceanofPDF.com) Chenwei Wang University of Electronic Science and Technology of China, Chengdu, Sichuan, China Jifang Pei University of Electronic Science and Technology of China, Chengdu, Sichuan, China Yulin Huang University of Electronic Science and Technology of China, Chengdu, Sichuan, China ISBN 978-981-95-3677-1 e-ISBN 978-981-95-3678-8 [https://doi.org/10.1007/978-981-95-3678-8](https://doi.org/10.1007/978-981-95-3678-8) © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026 This 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. 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 [OceanofPDF.com](OceanofPDF.com) Foreword Synthetic aperture radar (SAR) has become firmly established as one of the most vital sensing modalities in modern remote sensing. Through its unique all-day, all-weather imaging capabilities, SAR plays an indispensable role in applications from maritime monitoring to disaster assessment. SAR’s rapid deployment in space and airborne platforms has led to an explosion in the volume of SAR data, making automatic target recognition (ATR) a critical technology for efficient interpretation. Recent years have witnessed remarkable progress in SAR ATR driven by deep learning, yet these data-hungry models often encounter a bottleneck in operational scenarios: the scarcity of high-quality labeled data. Data limitations fundamentally constrain model generalization and stability. This problem, widely recognized throughout the pattern recognition community, is not merely an engineering hurdle. It presents a theoretical challenge that demands rethinking how we model intelligence under uncertainty. In this context, Synthetic Aperture Radar Target Recognition Under Limited Training Data: Theory and Methods offers a timely and rigorous response to these challenges. Instead of relying solely on conventional data augmentation or statistical regularization, Drs. Chenwei Wang, Jifang Pei, and Yulin Huang take a distinctive approach by integrating causal inference into the recognition framework. This monograph treats limited training data for SAR ATR not just as a fitting problem, but as a challenge in modeling, inference, and explanation. By explicitly distinguishing intrinsic causal features from spurious correlations, this book provides a systematic framework to understand why recognition fails in data-scarce environments and how it can be improved throu

FAQs

What problem does the book address in SAR target recognition?

It addresses automatic target recognition under limited training data, where scarce labeled samples hinder model generalization and stability in real operational scenarios.

How does the book approach limited training data differently from common solutions?

Instead of relying only on data augmentation or statistical regularization, it integrates causal inference into the recognition framework.

Why is distinguishing causal features from spurious correlations important?

The book uses this distinction to explain why recognition fails in data-scarce environments and to provide principled guidance for improving performance.

Aug 7, 2026

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