A target recognition method for SAR images based on feature fusion

Synthetic Aperture Radar (SAR) images comprise target areas, shadow regions, and background clutter. The target and shadow regions play a crucial role in target recognition and classification in SAR images. The existing SAR ATR method does not effectively separate the target region and the shadow region in the SAR image and extract the depth features separately, which makes the accuracy can not be further improved. This paper proposes a network method that separates target and shadow regions, extracts features from each, and then integrates them after segmentation. It solves the problem that two kinds of SAR image can not be extracted separately, and the recognition degree is improved to some extent compared with the existing methods. The method consists of three stages. Firstly, the original image undergoes threshold processing, counting filtering, morphological operations, contour extraction, etc., resulting in images containing only target or shadow regions, which are then cropped. Secondly, the preprocessed dataset undergoes data augmentation through affine transformation. The augmented target and shadow region images are then fed into parallel network structures for feature extraction. Finally, the features extracted from the two regions are fused, resulting in the ultimate classification decision. The effectiveness of the proposed method is validated through experiments using the MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset.