Weakly Annotated Residential Area Segmentation Based on Attention Redistribution and Co-Learning

Residential area (RA) segmentation is of great significance in the remote-sensing (RS) field. Training the segmentation network with image-level weakly annotated data (WAD) has become a research hot spot due to the easy access to classification labels. The quality of class activation maps (CAMs) is crucial for obtaining accurate segmentation results. Limited to irregular shapes, tortuous boundaries, and greatly varied scales of targets in RS images, generating high-quality CAMs is still a great challenge. To solve these problems, a novel weakly annotated RA segmentation model based on attention redistribution and co-learning (ARC) is proposed in this letter. We develop aggregate-and-distribute-based feature coupling (ADFC) to achieve the redistribution of attention on channel and spatial dimensions, which deals with multilevel features at the same time and makes them fully embedded together. Such an arrangement can effectively capture the shape characteristic of targets and filter out complicated backgrounds. To mitigate the impact of ambiguous regions like surroundings of boundaries and potential scattered houses, a confusion co-learning (CCL) strategy is designed to jointly explore the class-specific features and refine the cross-class features through a two-stream classifier with sharing weights, which helps generate sharper edges and discover ignored targets. Experimental results on GeoEye-1, SPOT5, and Landsat8 datasets reveal that our proposal outperforms the competing methods by a large margin in both subjective and objective assessments.