LOCAL-GLOBAL SIAMESE NETWORK WITH EFFICIENT INTER-SCALE FEATURE LEARNING FOR CHANGE DETECTION IN VHR REMOTE SENSING IMAGES

The popular networks for change detection (CD) in very-high-resolution (VHR) remote sensing (RS) images usually suffer from two problems. First, it is difficult for these networks to model simultaneously the local and global features of changed targets, which leads to the limited feature representation ability of popular CD networks. Second, these networks often have a large number of parameters and high computational costs due to complex network architecture. To address the above issues, we propose a local-global siamese network (LGS-Net) for CD in VHR RS images. First, we design an encoder with a parallel dual-branch structure consisting of convolutional neural networks (CNNs) and Transformer to extract rich features from bi-temporal images. Furthermore, we design a local-global feature enhancement (LGFE) module to help our encoder improve its feature representation ability. Second, we design a compact and efficient convolution module called inter-scale separable convolution (ISSConv). This module first divides feature maps into multiple groups, and then performs depthwise separable convolution in each group using atrous convolution with different dilation rates, which can not only capture changed targets across scales but also effectively reduce the number of model parameters. Experiments demonstrate that the proposed LGS-Net is superior to the state-of-the-art CD networks in terms of parameters, computational costs, and detection accuracy.