Global Context Dependencies Aware Network for Efficient Semantic Segmentation of Fine-Resolution Remoted Sensing Images

Abstract

Geospatial object segmentation is a fundamental task in remote sensing image interpretation. Although deep learning has shown great potential for this task, it often suffers from limited receptive fields, insufficient global feature extraction ability, and inaccurate edge positioning, resulting in low accuracy and errors in the results. In this letter, we propose a novel Global Context Dependency Awareness Network (GCDNet) to achieve high-accuracy segmentation results. To overcome the limited receptive fields and promote feature extraction ability, we propose a new dot product attention mechanism to establish long-distance dependencies between different receptive field feature maps. To achieve more accurate object edges, we design an edge-aware optimization module to guide the operation to directly optimize the edge details from the prediction result at the pixel level. Extensive experiments on two well-known public high-resolution remote sensing image datasets have been conducted to verify the performance of the proposed method, and the results show that the proposed method has significant advantages and maintains its robustness in different cases. Code will be available at: https://github.com/Cuiadd/GCDNet.