CSNet: A Remote Sensing Image Semantic Segmentation Network Based on Coordinate Attention and Skip Connections

作者：Jiahao Li, Hongguo Zhang, Liang Chen, Binbin He, Huaixin Chen · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17122048 · 被引用次数：8 · 研究领域：Remote-Sensing Image Classification、Advanced Image and Video Retrieval Techniques、Advanced Image Fusion Techniques

In recent years, the continuous development of deep learning has significantly advanced its application in the field of remote sensing. However, the semantic segmentation of high-resolution remote sensing images remains challenging due to the presence of multi-scale objects and intricate spatial details, often leading to the loss of critical information during segmentation. To address this issue and enable fast and accurate segmentation of remote sensing images, we made improvements based on SegNet and named the enhanced model CSNet. CSNet is built upon the SegNet architecture and incorporates a coordinate attention (CA) mechanism, which enables the network to focus on salient features and capture global spatial information, thereby improving segmentation accuracy and facilitating the recovery of spatial structures. Furthermore, skip connections are introduced between the encoder and decoder to directly transfer low-level features to the decoder. This promotes the fusion of semantic information at different levels, enhances the recovery of fine-grained details, and optimizes the gradient flow during training, effectively mitigating the vanishing gradient problem and improving training efficiency. Additionally, a hybrid loss function combining weighted cross-entropy and Dice loss is employed. To address the issue of class imbalance, several categories within the dataset are merged, and samples with an excessively high proportion of background pixels are removed. These strategi...