A New Structural Relation Extraction Framework for SAR Occluded Target Recognition

Partially occluded target recognition is a pressing issue in synthetic aperture radar (SAR) target recognition. Occlusion causes the loss of crucial information, like target structure details. This paper proposes a new structural relation extraction framework to address partial occlusion. It is achieved through the tailored design of counterfactual samples synthesizing and jigsaw mutual learning (CSS-JML). The ASC model parameters have clear physical meanings, aiding in understanding local structural changes. By integrating SAR and ASC images, effective structural relationship representations are extracted, mitigating occlusion effects. The CSS module is designed to generate occluded counterfactual SAR and ASC images using pairs of target data. There is no longer a requirement for further annotation information because this new generation process is limited by recognition tasks. The JML module employs mutual learning to complete jigsaw puzzle tasks in both modalities. And in this process, we design two types of similarity constraints to facilitate the extraction of unified structural information across different modalities. The FA module interacts with recognition features, facilitating the classification and identification of partially obscured targets. Experimental results on MSTAR-based and FUSARShip-based test datasets with three occlusion patterns demonstrate the method’s superiority in most occluded conditions, confirming its effectiveness in SAR occluded target recognition. Note to Practitioners—The motivation for this paper stems from the need to design a robust and efficient SAR target recognition method under partial occlusion. The structural relationships within samples can provide valuable information for recognizing occluded targets. We designed a counterfactual generation module to generate occluded target samples using paired targets in an unsupervised manner. This module is jointly optimized with subsequent recognition tasks, without the need for additional information. Recognizing the interaction bottleneck in mutual learning tasks, we designed two similarity constraints to promote the extraction of unified representations. Finally, we designed a feature affinity module to introduce structural representation information into the recognition branch. The proposed method achieves excellent classification performance in various occlusion environments.