RSCAC-NET: A Remote Sensing Image Change Description Network Based on Change-Aware and Multi-stage Global Fusion

Hongyi Dong, Xiuzhen He, Yan Wang, Jing liu, Feilong Bao, Bing Jia

Abstract: Analyzing land cover changes using multi-temporal remote sensing images is of great significance for environmental protection and land planning. However, traditional remote sensing change detection methods cannot directly reveal high-level semantic information such as the attributes of objects within the change regions and the relationships between these objects. Therefore, this paper proposes a Change Description Network called RSCAC. The network is composed of a feature extractor (ResNet101), a Change-aware Attention Module (CAAM), a Multi-Stage Global Fusion Module (MSGFM), and a description decoder. The Change-aware Module integrates a similarity module and a cross-attention mechanism. The Multi-Stage Global Fusion Module utilizes an innovative global fusion mechanism to effectively merge and extract global visual feature representations, enabling a more comprehensive description of the entire change scene. Comparative experiments on the LEVIR-CC dataset demonstrate that RSCAC can generate more coherent, accurate, and comprehensive change descriptions. Compared to the recently well-performing Chg2Cap, RSCAC achieves improvements of 2.05%, 0.17%, and 1.92% in BLEU-4, METEOR, and CIDEr-D, respectively. The visualization results also show that our model can focus on the changes of interest while ignoring irrelevant changes.

External IDs:doi:10.1007/978-3-032-10459-5_28