Feature alignment and refinement for Remote Sensing images change Detection

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.

KEYWORDS:

• Change detection (CD)
• minor change
• feature alignment
• feature refinement
• cross-temporal feature interaction module

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 62176139, and in part by the Major Basic Research Project of Natural Science Foundation of Shandong Province under Grant ZR2021ZD15.