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

作者：Man Chen, Tianfeng Wang, Chengcheng Xu, Jun Chen, Enping Chen, Zhisong Pan · 发表于：IEEE Sensors Journal · 年份：2024 · DOI：10.1109/jsen.2024.3467030 · 被引用次数：13 · 研究领域：Advanced SAR Imaging Techniques、Synthetic Aperture Radar (SAR) Applications and Techniques、Robotics and Sensor-Based Localization

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 segmentati...