Gradient Prior Guidance and Image Adaptation Enhancement for Semi-Supervised SAR Ship Instance Segmentation

The instance segmentation of ships in synthetic aperture radar (SAR) images aims to interpret detailed position and shape information, holding significant potential applications in ocean-going ship monitoring and port scheduling. Existing SAR ship instance segmentation methods face challenges such as expensive label production costs, weak edge detail perception, and insufficient adaptation to intrinsic limitations in SAR images, such as object information loss and speckle noise. Addressing these challenges, we propose a gradient prior guided and SAR image adaptation enhanced semi-supervised instance segmentation (GGSE-SSIS) method. This method, rooted in a teacher-student framework, leverages pseudo-labels generated by a teacher model trained on a small amount of data to guide the student model toward mask prediction, thus achieving high-performance instance segmentation of SAR ships at low annotation costs. We have also meticulously designed a gradient prior guidance (GPG) module to enhance the gradient consistency between the objects and the corresponding mask proposals, facilitating the perception of target edge details. Additionally, the SAR image adaptation enhancement (SIAE) operation is introduced into the GGSE-SSIS method to construct more robust training signals while enhancing adaptability to intrinsic limitations such as object information loss and speckle noise in SAR images. Experimental results on high-resolution SAR images dataset (HRSID) and polygon segmentation SAR ship detection dataset (PSeg-SSDD) demonstrate that the proposed GGSE-SSIS achieves segmentation performance close to that of fully supervised methods using only 30% pixel-level annotations, effectively balancing annotation costs and visual perceptual effects in the SAR ship instance segmentation task.