Integrating bi-temporal VHR optical and long-term SAR images for built-up area change detection

ABSTRACT

With the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

KEYWORDS:

• Built-up area
• change detection
• deep learning
• long-term SAR
• very high-resolution image

Disclosure statement

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

Data availability statement

The dataset and the code of HOLS-CDnet is available at https://github.com/Lihy256/HOLS-CDnet.

Additional information

Funding

This study was supported in part by the National Key R&D Program of China under Grant 2022YFB3903402, in part by the National Natural Science Foundation of China under Grant 42222106, in part by the National Natural Science Foundation of China under Grant 61976234, and in part by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University under Grant 22lgqb12.