Self-supervised remote sensing image change detection based on high frequency feature and gate attention-guided optimization unit

Abstract

Remote sensing image change detection (CD) is a technical method to analyze and compare remote sensing images covering the same area with different phases to determine the process of surface change, which has important application value in natural resource management, land monitoring, and natural disaster monitoring. At present, the commonly used supervised remote sensing image CD technique requires a large number of labeled samples to train the network, which brings a large human and material cost. In contrast, the unsupervised remote sensing image CD technique does not require the construction of labeled samples; however, there is no direct correspondence between the unsupervised learning task and the downstream CD. To address the aforementioned issues, a self-supervised remote sensing image CD method based on a high frequency feature enhancement module (HFFEM) and gate attention-guided optimization unit (GAGOU) Siamese-like network is proposed. First, the network is trained using a self-supervised learning diagram to extract features that are beneficial to remote sensing image CD and better serve the downstream CD task. After that, a clustering loss function and a contrastive loss function are used to optimize the network. Second, to enhance the feature extraction capability for change edges and to address the degradation of network performance due to the entrainment of redundant features during feature fusion, HFFEM and GAGOU are proposed, respectively. Finally, comprehensive simulation experiments were conducted on the IKONOS multi-spectral datasets Mina and Riyadh, the open CD dataset Onera satellite change detection (OSCD), and the heterogeneous dataset Shuguang, showing the effectiveness of the proposed algorithms based on evaluation metrics, such as overall accuracy, kappa coefficient, and F1 score.