SSGL—Pixel Level SAR Ship Instance Segmentation Network Based on Global and Local Feature Cross Attention

Synthetic Aperture Radar (SAR) plays a crucial role in maritime search, rescue operations and port vessel traffic monitoring. Existing algorithms are difficult to simultaneously extract features of multi-scale targets in SAR images, resulting in uneven accuracy in multi-scale ship instance segmentation. Moreover, due to the complexity of image scenes, existing algorithms struggles to accurately segment targets. Regarding the above difficulties, we propose a Pixel level SAR ship instance segmentation network based on global and local feature cross attention (SSGL). We propose a context aware convolutional attention module (CACA). CACA leverages cross-correlation calculations for global information, aiding SSGL in better distinguishing between foreground objects and complex backgrounds. We design a channel optimization module (COM) that combines multi-path convolution with channel attention to adaptively adjust the receptive field, allowing for balanced feature extraction across different target scales. SSGL’s instance segmentation mask achieved 93.6 on the SSDD dataset and 89.1 on the HRSID dataset. Both AP
M
and AP
L
significantly surpasses comparative algorithms, proving SSGL's balanced and high-precision instance segmentation for multi-scale targets.