SSCFNet: A Spatial-Spectral Cross Fusion Network for Remote Sensing Change Detection
Jiahao Wang, Fang Liu, Licheng Jiao, Hao Wang, Hua Yang, Xu Liu, Lingling Li, Puhua Chen
Abstract
Convolutional Neural Networks(CNNs) are data-driven methods that automatically extract the rich information embedded in remote sensing images. However, most current deep learning-based remote sensing image change detection methods prioritize high-level semantic features, while not enough attention is given to low-level semantic features, resulting in the loss of edges and details of the change region. To address this problem, this paper constructs a spatial-spectral cross fusion network SSCFNet, divided into three modules: a feature extractor network module, a combined enhancement module, and a semantic cross-fusion module. A new combined enhancement strategy is proposed to construct several semantic feature blocks in the combined enhancement module. Different convolution operations are applied to the newly constructed semantic feature blocks in the semantic cross fusion module, and the obtained semantic features at various levels are cross-fused. Experiments show that the proposed SSCFNet outperforms the other six state-of-the-art methods on four publicly available remote sensing image change detection datasets.