Feature alignment and refinement for Remote Sensing images change Detection

Abstract

ABSTRACT Change detection (CD) plays a critical role in extracting ground changes from bi-temporal remote sensing (RS) images and is instrumental in understanding surface dynamics. In recent years, deep learning has made significant breakthroughs in CD. However, typical CD methods that employ the Siamese network for temporal feature extraction lack feature alignment ability for bi-temporal heterogeneous RS images, resulting in inadequate temporal discriminative capability. Moreover, deep learning-based CD methods are still susceptible to the problem of minor changes missing due to scale variation with deeper network layers. In this article, we propose a bi-temporal feature alignment and refinement network (FARNet). To improve the discriminative capability of the Siamese network, an adversarial learning-based temporal discriminatory loss function is designed to align temporal-level features and eliminate bi-temporal domain shift, and a cosine similarity-based loss function is employed to measure feature distance at the Pixel-level. To address the problem of minor changes missing, we adopt a dilated convolution-based Siamese network to prevent feature map size reduction, and a multi-level feature detail supplement (MFDS) module is designed to supplement the deep layer features with shallow layer features. Additionally, we construct a change map refinement (CMR) module that refines the coarse change map to the fine-grained change map. Furthermore, we design a cross-temporal feature interaction (CFI) module to learn more fine-grained change features by combining features across temporal. Comprehensive experimental results on two popular CD datasets demonstrate the effectiveness and efficiency of FARNet compared with state-of-the-art (SOTA) methods.