Fifty Years of Object Detection and Recognition from Synthetic Aperture Radar Remote Sensing Imagery: The Road Forward

Abstract

Synthetic Aperture Radar (SAR) imaging is capable of observing objects in nearly all weather and illumination conditions and has become an indispensable means of information acquisition for analysis and recognition of objects and scenes. SAR Automatic Target Recognition (SAR ATR) has been one of the most fundamental and challenging problems in remote sensing image analysis. Nowadays, the AI technology, represented by large models and AI agents, has transformed the research paradigm, profoundly influenced various research fields, and continues to evolve at an unprecedented pace. However, the huge potential of AI for SAR image analysis remains locked. To unlock the potential of AI in SAR image understanding, the research community should rethink how to enable bidirectional empowerment between AI and SAR image understanding and strive to achieve substantial breakthroughs at critical bottlenecks. Given this period of remarkable evolution, this paper offers the first comprehensive review of SAR ATR, tracing its development and milestones over the past five decades and providing the research community with a clear roadmap. This survey includes approximately 250 research contributions, covering critical aspects of SAR ATR: pivotal challenges, important datasets, the merits and limitations of representative methods, evaluation metrics, and state of the art performance. Finally, we finish the survey by identifying promising directions for future research. Looking ahead, we call for significant attention on three fundamental pillars: the curation of high-quality large-scale datasets, the design of fair and comprehensive evaluation benchmarks, and the fostering of safe open-source ecosystems.

Figures (12)

• Figure 1: Importance of SAR ATR. (a) From 2020 to 2024, the annual number of published papers in the fields of remote sensing (RS) and computer vision (CV) has reached a similar level (the gap is less than 10%). However, the number of public RS-related code repositories on GitHub remains relatively limited, accounting for only approximately one-fourth of that in the CV domain. This discrepancy highlights significant untapped potential for advancing open-source ecosystem development within the remote sensing community. (b) Most frequent keywords in remote sensing-related papers from 2020 to 2024. The size of each word is proportional to the frequency, highlighting that concepts such as synthetic aperture radar (SAR), image classification, and object detection have garnered substantial attention. (c) SAR ATR are widely used and irreplaceable for polar sea ice monitoring and navigation safety (in the field of glaciers), in extraterrestrial geology and target recognition (in deep space exploration), forest/flood/deformation monitoring related to global change, and also situational awareness for public safety and national defense. As a core direction in the intelligent interpretation of remote sensing images, SAR ATR has been continuously attracting high attention from both the academic and industrial communities. (All statistics on the number of papers are from the WOS Core Collection Database.)
• Figure 2: Timeline milestone of SAR automatic target recognition evolution, including two core tasks of classification and detection, from understanding physics, designing features, learning features to understanding and learning features. (Classification: SAR wiley1985synthetic, SAR Imaging harger1971synthetic, SAR ATR novak1993performance, MSTAR keydel1996mstar, ASC potter1997attributed, Texture holmes1998textural, SVM zhao2002SVMATR, Fisher-MC tison2004newMK, Adaboost sun2007adaboost, Sparse Representation zhang2012multiJSR, SIFT-SAR dellinger2014sarSIFT, A-ConvNets chen2016target, CV-CNN zhang2017complexCVCNN, WWH huang2019WWH, FEC zhang2020fec, HOG-ShipCLSNet zhang2021hogShip, CA-MCNN li2021CAMCNN, PIHA huang2022piha, VSFA zhang2023vsfa, EMI-Net liao2024eminet, SARATR-X li2025saratrx. Detection: CFAR finn1966adaptiveCACFAR, K Distribution jakeman1976KDistribution, Two pa.CFAR novak1989studies, Go Distribution frery1997model, WaveletDet tello2005WaveletDet, FuzzyLogic OSSD keramitsoglou2006autoOil, AOSDS solberg2007oil, SARDet 2008gaoguidetectionsurvey2009gaoguidiscriminationsurvey, Bi-CFAR leng2015bilateralCFAR, SSDD li2017shipssdd, SER FRCNN lin2018SERFasterRCNN, DAPN cui2019dapn, DBBox-v2 an2019drboxV2, FBR-Net Fu2020FERNet, Centernet++ guo2021centernet++, SEFEPNet zhang2022sefepnet, SAR-AIRCraft 1.0 zhirui2023sarcraft, DiffDet4SAR zhou2024diffdet4sar, EarthGPT zhang2024earthgpt, ASC-U2Det wang2025ASCU2Det.)
• Figure 3: The taxonomy of representative methods in SAR ATR.
• Figure 4: (a) Definition of SAR ATR. It encompasses two key stages: detection, which locates potential target regions within a large-scale SAR image, and classification, which classifies the specific category (exemplified by the oil tanker ship) of the detected target. (b) Difference between optical and SAR images, and some challenging instances during SAR target recognition.
• Figure 5: Main Challenges of SAR ATR.