This book reports the latest results in the study of synthetic aperture radar (SAR) automatic target recognition (ATR) and focuses on the theory and methods of limited-data SAR ATR, and experimental verification, and many other aspects. In practical applications, due to the scarcity of targets, the difficulty of SAR image acquisition, and the difficulty of accurate labeling. It is impossible to obtain sufficient SAR images. Thus, it is one of the core issues of concern in the field of SAR ATR. This book contains two main parts: classical limited-data SAR ATR and limited-data SAR causal ATR. The first part consists of theoretical foundations, and limited-data SAR ATR based on data augmentation and model design. The second part focuses on the theoretical foundations of SAR causal ATR, and methods based on causal feature extraction and causal intervention. This book also includes the research results of realization technology and experimental verification. Researchers, engineers, and graduate students in image processing and deep learning can benefit from this book, who want to learn the core theories, methods, and applications of SAR ATR.

Explores limited-data SAR ATR, covering classical & causal approaches, theory, data augmentation, and design Combines experiments with SAR ATR research, tackling data limits, target scarcity, and image acquisition challenges Covers core theories, methods, and applications to advance SAR ATRFor researchers, engineers, and graduate students

Autorentext

Chenwei Wang received his B.S. degree in Electronic Engineering and his Ph.D. degree in Information and Communication Engineering in 2023, both from the University of Electronic Science and Technology of China (UESTC). From 2022 to 2023, he was a joint Ph.D. Student with the Department of Computing, National University of Singapore, Singapore. He is currently a postdoctoral research fellow at the CityU-Oxford joint center for intelligent multidimensional data analysis, Hong Kong. His research interests include radar signal processing, machine learning, and automatic target recognition.

Jifang Pei received the B.S. degree from the Xiangtan University, Xiangtan, Hunan, China, in 2010, and the M.S. and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2013 and 2018, respectively. From 2016 to 2017, he was a joint Ph.D. Student with the National University of Singapore, Singapore. He is currently a Research Fellow with the School of Information and Communication Engineering, UESTC. His research interests include radar target recognition, microwave remote sensing, and machine learning.

Yulin Huang received his B.S. degree in 2002 and his Ph.D. degree in 2008, both from the University of Electronic Science and Technology of China (UESTC). He is currently a Professor of the School of Information and Communication Engineering at the UESTC. His research interests include radar signal processing, target detection and recognition, and artificial intelligence. He has published over 150 papers, including 90 SCI-indexed papers and several ESI Highly Cited Papers, and holds 90+ national invention patents. He serves on the Editorial Boards of several journals and as TPC Members for several international conferences.

Inhalt

Introduction.- Theoretical Foundations of Limited-Data SAR ATR.- Limited-Data SAR ATR Methods Based on Data Augmentation.- Limited Data SAR ATR Methods Based on Model Design.- Theoretical Foundations of Limited Data SAR Causal ATR.- Limited-Data SAR ATR Method Based on Causal Feature Extraction.- Limited Data SAR ATR Based on Causal Intervention.- Conclusion and Future Directions.