T-UNet: triplet UNet for change detection in high-resolution remote sensing images

Abstract

ABSTRACTRemote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

Keywords

• Change detection
• remote sensing
• deep learning
• triplet encoder
• attention mechanism