FTRNet: triplet fusion temporal relationship network for change detection in bitemporal remote sensing images

Abstract

Change detection (CD) in remote sensing (RS) images aims to identify surface changes based on images acquired at different times. However, existing methods are still unsatisfactory in locating fine details of change in RS images, due to overlooking the inherent temporal information. To address the issue, we introduce a novel Triplet Fusion Temporal Relationship Network (FTRNet). FTRNet incorporates a triplet input backbone that enables the extraction of both spatial and temporal features. We design a change attention module to enhance bitemporal features, making the backbone network retain temporal information and fuse cross-scale features to extract the high-level location information. We evaluate our method on three benchmark datasets, including LEVIR-CD, WHU-CD, GZ, and DSIFN. The experimental results showcase that FTRNet achieves IoU scores of 83.60%, 77.06%, 73.67%, and 77.30% in LEVIR-CD, WHU-CD, GZ, and DSIFN datasets, respectively. These results surpass the second-best baseline by 1.20%, 0.49%, 1.31%, and 1.20%, respectively.

Keywords

• Change detection
• remote sensing
• triple network
• transformer
• attention mechanism