Context Aggregation Network for Remote Sensing Image Semantic Segmentation

In recent years, remote sensing technology has been widely applied in various industries, and semantic segmentation of remote sensing images has attracted much attention. Due to the complexity and special characteristics of remote sensing images, multi-scale object detection and accurate object localization are important challenges in remote sensing image semantic segmentation. Therefore, this paper proposes a context aggregation network (CANet). The design of CANet is influenced by advanced technologies such as attention mechanisms and feature fusion and enhancement. This network first introduces nested dilated residual module (NDRM), which can fully utilize the features extracted by the backbone network. Then, improved integrated successive dilation module (IISD) is proposed to effectively aggregate a series of contextual information scales. Next, Swim Transformer module is embedded to provide global contextual information. Finally, multi-resolution fusion module (MRFM) is proposed, allowing the comprehensive fusion of feature layers from different stages of the encoder, preserving more semantic and detailed information. The experimental results show that CANet outperforms other advanced models on the Potsdam and Vaihingen datasets.