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Improving Building Segmentation for Off-Nadir Satellite Imagery

arXiv 2109.03961 ↗First posted 8 Sep 2021cs.CVcited by 0

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Abstract (arXiv)

Automatic building segmentation is an important task for satellite imagery analysis and scene understanding. Most existing segmentation methods focus on the case where the images are taken from directly overhead (i.e., low off-nadir/viewing angle). These methods often fail to provide accurate results on satellite images with larger off-nadir angles due to the higher noise level and lower spatial resolution. In this paper, we propose a method that is able to provide accurate building segmentation for satellite imagery captured from a large range of off-nadir angles. Based on Bayesian deep learning, we explicitly design our method to learn the data noise via aleatoric and epistemic uncertainty modeling. Satellite image metadata (e.g., off-nadir angle and ground sample distance) is also used in our model to further improve the result. We show that with uncertainty modeling and metadata injection, our method achieves better performance than the baseline method, especially for noisy images taken from large off-nadir angles.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 8 Sep 2021.

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27 of 42 references.

References · 27

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15 more
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