Consistency-guided lightweight network for semi-supervised binary change detection of buildings in remote sensing images

Precise identification of binary building changes through remote sensing observations plays a crucial role in sustainable urban development. However, many supervised change detection (CD) methods overly rely on labeled samples, thus limiting their generalizability. In addition, existing semi-supervised CD methods suffer from instability, complexity, and limited applicability. To overcome these challenges and fully utilize unlabeled samples, we proposed a consistency-guided lightweight semi-supervised binary change detection method (Semi-LCD). We designed a lightweight dual-branch CD network to extract image features while reducing model size and complexity. Semi-LCD fully exploits unlabeled samples by data augmentation, consistency regularization, and pseudo-labeling, thereby enhancing its detection performance and generalization capability. To validate the effectiveness and superior performance of Semi-LCD, we conducted experiments on three building CD datasets. Detection results indicate that Semi-LCD outperforms competing methods, quantitatively and qualitatively, achieving the optimal balance between performance and model size. Furthermore, ablation experiments validate the robustness and advantages of the Semi-LCD in effectively utilizing unlabeled samples.

Automated summary

This study proposes a lightweight semi-supervised binary change detection method (Semi-LCD) that achieves the optimal balance between performance and model size by fully utilizing unlabeled samples. By employing data augmentation, consistency regularization, and pseudo-labeling, Semi-LCD enhances detection performance and generalization capability. Experimental results demonstrate that Semi-LCD outperforms competing methods in terms of quantitative and qualitative measures. Ablation experiments further validate the robustness and advantages of Semi-LCD in effectively utilizing unlabeled samples.