LAUSR

Due to the existence of speckle noise, background clutter, backscattering points, and geometric distortion of some targets in synthetic aperture radar (SAR) images, extracting multiscale and multilocation targets accurately is still a great challenge. To tackle these problems, a novel target detection method based on edge-aware and cross-coupling attention for SAR images is proposed in this letter. By enhancing the dependencies between targets in different locations, bridging the gap between different feature maps, and assisting the targets’ detection through cross-coupling with the edge-aware network, the performance of detecting multiscale targets in complex SAR images can be improved significantly. Specifically, residual spatial pyramid pooling (RSPP) and mixed pooling module (MPM)-based convolution block attention module (MCBAM) are combined in the decoding part to promote coupling between networks. Besides, the semi-dense connection is adopted in the encoding part based on residual convolution block (RCB), which can improve the ability of multiscale feature extraction and promote the acquirement of high-resolution features with strong semantic information. Experiments are conducted on the SAR oil tank dataset (OTD) and SAR residential area dataset (RAD). We compare our model with a traditional method and CNN-based algorithms. The experimental results verify that our model outperforms the competing models in both pixel level and geometric segmentation accuracy.