Improving Building Segmentation Using Uncertainty Modeling and Metadata Injection
| Autor: | Sriram Baireddy, Latisha Konz, Hanxiang Hao, Mary L. Comer, Kevin J. LaTourette, Moses W. Chan, Edward J. Delp |
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| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Ground sample distance Viewing angle Metadata Computer Science::Computer Vision and Pattern Recognition Overhead (computing) Segmentation Computer vision Artificial intelligence Uncertainty quantification Focus (optics) business Image resolution |
| Zdroj: | SIGSPATIAL/GIS |
| DOI: | 10.1145/3474717.3483918 |
| Popis: | 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 angles1. |
| Databáze: | OpenAIRE |
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