Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images

Abstract

Change detection is an important task in the field of remote sensing. Various change detection methods based on convolutional neural networks (CNNs) have recently been proposed for remote sensing using satellite or aerial images. However, existing methods allow only the partial use of content information in images during change detection because they adopt simple feature-similarity measurements or pixel-level loss functions to construct their network architectures. Therefore, when these methods are applied to complex urban areas, their performance in terms of change detection tends to be limited. In this paper, a novel CNN-based change detection approach, referred to as a local similarity Siamese network (LSS-Net), with a cosine similarity measurement, has been proposed for better urban land change detection in remote sensing images. To use content information on two sequential images, a new change attention map-based content loss (CAC loss) function was developed in this study. In addition, to enhance the performance of LSS-Net in terms of change detection, a suitable feature-similarity measurement method, incorporated into a local similarity attention module, was determined through systemic experiments. To verify the change detection performance of LSS-Net, it was compared with other state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of F1 score (0.9630, 0.9377, and 0.7751), and kappa (0.9581, 0.9351, and 0.7646) on the three test datasets, thus suggesting its potential for various remote sensing applications. CCBY