Dual-branch network for change detection of remote sensing image

Abstract

Change detection is important in remote sensing image analysis. In recent years, significant breakthroughs have been made in change detection algorithms based on deep learning. However, due to continuous downsampling, the detection results of these algorithms still have serious detection errors, detection omissions and edge blurring. Aiming at these problems, this paper proposes a dual-branch network for change detection. The network has two branches, which are used to extract the depth-variant semantic features of the multi-temporal image pairs and the respective features of each image respectively. In addition, we designed a Multi-scale Strip Convolution Module (MSCM) to extract the multi-scale features of the image, a new Spatial Attention Module (SAM) to strengthen the feature representation of changing regions, and a Feature Fusion Network (FFN) to guide the fusion between multiple features of the two branches. Experimental results show that the proposed method substantially mitigates detection errors, detection omissions and obtains sharper edges, it outperforms other current algorithms.