Deep Multiscale Siamese Network With Parallel Convolutional Structure and Self-Attention for Change Detection

Abstract

With the wide application of deep learning (DL), change detection (CD) for remote-sensing images (RSIs) has realized the leap from the traditional to the intelligent methods. However, many existing methods still need further improvement in practical applications, especially in increasing the effectiveness of feature extraction and reducing the model computational cost. In this article, we propose a novel deep multiscale Siamese network with parallel convolutional structure (PCS) and self-attention (SA) (MSPSNet), which has excellent capabilities of feature extraction and feature integration under an acceptable consumption. It mainly contains three subnetworks: deep multiscale feature extraction, feature integration by the PCS, and feature refinement based on the SA. In the first subnetwork, a deep multiscale Siamese network based on convolutional block is designed to depict the image features at different scales for different temporal images. In the subsequent subnetworks, a PCS model is proposed to integrate multiscale features of different temporal images, and then, an SA model is constructed to further enhance the representation of image information. Experiments are conducted on two public RSI datasets, indicating that the proposed framework performs well in detecting changes.