An improved method for rapid un-collapsed building extraction from post-disaster high-resolution remote sensing imagery based on multi-scale feature alignment

ABSTRACT

Quick and accurate extraction of un-collapsed buildings from post-disaster High-resolution Remote Sensing Images (HRSIs) is imperative for emergency response. Pre-disaster HRSIs could serve as auxiliary data for training models to expedite this extraction process. However, the effectiveness of models trained directly on pre-disaster HRSIs tends to diminish when applied to post-disaster scenarios, mainly due to the notable discrepancies between these datasets. The current popular approach to mitigate this issue involves aligning features from pre- and post-disaster images using an unsupervised domain adversarial learning framework. However, conventional methods often fall short in reducing the substantial disparity between pre- and post-disaster images, due to a lack of comprehensive alignment of category-level and multi-scale features. To overcome these limitations, we propose the Multi-scale Global and Category-attention Features Alignment Network (MGCAN). This novel approach further refines feature alignment strategies by concurrently aligning both multi-scale global and category-attention features, thus effectively narrowing the gap between pre- and post-disaster HRSIs. Extensive experiments have demonstrated that MGCAN significantly improves the accuracy of un-collapsed building extraction from post-disaster HRSIs. Moreover, compared to other state-of-the-art domain adversarial networks, MGCAN exhibits superior performance in different disaster scenarios.

KEYWORDS:

• Post-disaster
• un-collapsed building extraction
• category-attention feature
• multi-scale feature
• unsupervised domain adversarial

Acknowledgements

We thank the anonymous reviewers for their constructive suggestions to help us improve the manuscript of this paper.

Disclosure statement

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

Data availability statement

The Tornado data that support the findings of this study are available on request. However, the Yushu earthquake data is not available because written consent was not given for it to be shared publicly.

Additional information

Funding

This work was supported by the National Key Research and Development Project of China under Grant 2017YFB0504102.