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

ABSTRACT

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.

KEYWORDS:

• Building change detection
• remote sensing images
• semi-supervised learning
• consistency regularization
• lightweight network

Disclosure statement

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

CRediT authorship contribution statement

Qing Ding: Methodology, Software, Writing – original draft. Zhenfeng Shao: Methodology, Data curation, Visualization. Xiao Huang: Writing – review & editing. Xiaoxiao Feng: Validation, Visualization. Orhan Altan: Validation, Conceptualization. Bin Hu: Data curation.

Data availability statement

The MtS-WH dataset is available at http://sigma.whu.edu.cn/newspage.php?q=2019_03_26_ENG; the WHU Building dataset is available at http://gpcv.whu.edu.cn/data/building_dataset.html; the HRCUS-CD dataset is available from the corresponding author upon reasonable request.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42090012]; 03 special research and 5G project of Jiangxi Province in China [20212ABC03A09]; Hubei key R&D plan [2022BAA048]; Key R&D project of Sichuan science and technology plan [2022YFN0031]; Zhuhai industry university research cooperation project of China [ZH22017001210098PWC].