Bi-temporal Attention Transformer for Building Change Detection and Building Damage Assessment
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Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Building Change Detection (BCD) holds significant value in the context of monitoring land use, while Building Damage Assessment (BDA) plays a crucial role in expediting humanitarian rescue efforts post-disasters. To address these needs, we propose the Bi-Temporal Attention Module (BAM) as an innovative cross-attention mechanism aimed at effectively capturing spatio-temporal semantic relations between a pair of bi-temporal remote sensing images. Within BAM, a shifted windowing scheme has been implemented to confine the scope of the cross-attention mechanism to a specific range, not only excluding remote and irrelevant information but also contributing to computational efficiency. Moreover, existing methods for BDA often overlook the inherent order of ordinal labels, treating the BDA task simplistically as a multi-class semantic segmentation problem. Recognizing the vital significance of ordinal relationships, we approach the BDA task as an ordinal regression problem. To address this, we introduce a rank-consistent ordinal regression loss function to train our proposed change detection network, Bi-temporal Attention Transformer (BAT). Our method achieves state-of-the-art accuracy on two BCD datasets (LEVIR-CD+ and S2Looking), as well as the largest BDA dataset (xBD).Description
Keywords
4605 Data Management and Data Science, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, 15 Life on Land, 0406 Physical Geography and Environmental Geoscience, 0801 Artificial Intelligence and Image Processing, 0909 Geomatic Engineering, 3709 Physical geography and environmental geoscience, 4013 Geomatic engineering, 4601 Applied computing
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN: 1939-1404 (Print); 2151-1535 (Online), Institute of Electrical and Electronics Engineers (IEEE), PP(99), 1-20. doi: 10.1109/JSTARS.2024.3354310
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/