@article{CHEN2026, 
author = {Siwei CHEN and Linyu DAI and Xinchao WANG},
title = {Attention mechanism and intelligent processing of radar images: progress and prospects},
year = {2026},
journal = {Journal of National University of Defense Technology},
volume = {48},
number = {3},
pages = {36-51},
keywords = {attention mechanism, intelligent processing of radar images, deep learning},
url = {https://www.sciopen.com/article/10.11887/j.issn.1001-2486.26010035},
doi = {10.11887/j.issn.1001-2486.26010035},
abstract = {SignificanceSAR is capable of acquiring high-resolution two-dimensional radar images through azimuthal aperture synthesis and range pulse compression, presenting the geometric structure and scattering characteristics of the observed area intuitively. As an active microwave imaging remote sensing device, SAR features strong penetration capability, all-day and all-weather operation, and immunity to illumination and weather conditions, playing an irreplaceable role in military reconnaissance, disaster assessment, marine rights protection, and other critical fields. However, inherent speckle in radar images blurs target features and distorts texture details, while the coupling of target and background scattering properties in complex scenes significantly increases the difficulty of intelligent target recognition. Inspired by the human visual system’s selective attention mechanism, attention mechanisms have been introduced into deep learning-based radar image processing, enabling models to adaptively assign weights to focus on critical information and suppress redundant interference. Given the rapid development and wide application of attention mechanisms in this domain, a systematic review of relevant research progress is of great academic value and practical significance to promote continuous innovation and in-depth engineering application of radar image intelligent processing.ProgressThis paper systematically combed the development context of attention mechanisms, which can be divided into four stages: recurrent attention models, explicit spatial feature selection, channel-wise feature calibration, and self-attention dominated Transformer architectures. Typical attention models were categorized into four types: channel attention, spatial attention, self-attention, and hybrid attention, with their core principles and representative structures elaborated. On this basis, the innovative applications of various attention mechanisms were comprehensively reviewed in key radar image processing tasks, including preprocessing, target detection, image segmentation, target recognition, change detection, multi-modal fusion, and image restoration. To verify the practical performance of attention mechanisms in engineering scenarios, a comparative experiment was conducted on radar image target detection based on YOLOv11s, using HRSID and SAR-AIRcraft-1.0 datasets. Five representative attention mechanisms (GAM, RFAConv, CoT, SCSA, and MLCA) were evaluated in terms of precision, recall, mAP50, mAP50:95, model parameters, computational complexity, and real-time performance. Experimental results show that RFAConv achieves the highest performance gain in ship target detection, while GAM, MLCA, and CoT exhibit respective advantages in precision, recall, and multi-scale localization accuracy for aircraft target detection.Conclusions and ProspectsIn conclusion, attention mechanisms effectively enhance the feature learning ability and task performance of radar image intelligent processing by selectively focusing on critical information and suppressing clutter interference. Future research directions are prospected from four aspects: First, improve the interpretability of attention mechanisms by establishing the mapping relationship between attention weights and radar scattering physical mechanisms to break the black-box limitation of data-driven models. Second, design efficient attention architectures via sparse modeling, dimension decomposition, and physical prior embedding to meet the real-time and lightweight requirements of resource-constrained platforms such as airborne and spaceborne systems. Third, optimize attention mechanisms for multi-modal fusion by constructing dynamic weight allocation and cross-modal semantic correlation modeling to fully exploit complementary information among heterogeneous data sources. Fourth, develop physics-guided attention designs tailored for radar-specific foundation models to address representation bias and semantic gaps caused by direct migration of general vision Transformers, supporting the development of large-scale, high-performance radar remote sensing foundation models.}
}