Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

Abstract

Instance segmentation in remote sensing images is challenging due to the object-level discrimination and pixel-level segmentation for the objects. In remote sensing applications, instance segmentation adopts the instance-aware mask, rather than horizontal bounding box and oriented bounding box in object detection, or category-aware mask in semantic segmentation, to interpret the objects with the boundaries. Despite these distinct advantages, versatile instance segmentation methods are still to be discovered for remote sensing images. In this paper, an efficient instance segmentation paradigm (EISP) for interpreting the synthetic aperture radar (SAR) and optical images is proposed. EISP mainly consists of the Swin Transformer to construct the hierarchical features of SAR and optical images, the context information flow (CIF) for interweaving the semantic features from the bounding box branch to mask branch, and the confluent loss function for refining the predicted masks. Experimental conclusions can be drawn on the PSeg-SSDD (Polygon Segmentation—SAR Ship Detection Dataset) and NWPU VHR-10 instance segmentation dataset (optical dataset): (1) Swin-L, CIF, and confluent loss function in EISP acts on the whole instance segmentation utility; (2) EISP* exceeds vanilla mask R-CNN 4.2% AP value on PSeg-SSDD and 11.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The poorly segmented masks, false alarms, missing segmentations, and aliasing masks can be avoided to a great extent for EISP* in segmenting the SAR and optical images; (4) EISP* achieves the highest instance segmentation AP value compared to the state-of-the-art instance segmentation methods.