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Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping

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Abstract

Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost. The increasing availability of satellite imagery and other remote sensing data has triggered research activities on development of algorithms for detection and monitoring of natural events. Here, we introduce an unsupervised subspace learning-based methodology that uses multi-temporal and multi-spectral satellite images to identify regions damaged by natural disasters. It first performs region delineation, matching, and fusion. Next, it applies subspace learning in the joint regional space to produce a change map. It identifies the damaged regions by estimating probabilistic subspace distances and rejecting the non-disaster changes. We evaluated the performance of our method on seven disaster datasets including four wildfire events, two flooding events, and a earthquake/tsunami event. We validated our results by calculating the dice similarity coefficient (DSC), and accuracy of classification between our disaster maps and ground-truth data. Our method produced average DSC values of 0.833 and 0.736, for wildfires and floods, respectively, and overall DSC of 0.855 for the tsunami event. The evaluation results support the applicability of our method to multiple types of natural disasters.

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Funding

The research leading to these results received funding from the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (award #SC3GM113754) and by the Army Research Office under Grant #W911NF2010095.

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Authors and Affiliations

  1. Division of Physics, Engineering, Mathematics, and Computer Sciences, Delaware State University, 1200 N. DuPont Hwy, Dover, DE, 19901, USA

    Azubuike Okorie & Sokratis Makrogiannnis

  2. Department of Computer and Information Sciences, University of Delaware, 210 South College Avenue, Newark, DE, 19716, USA

    Chandra Kambhamettu

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  1. Azubuike Okorie
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  2. Chandra Kambhamettu
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  3. Sokratis Makrogiannnis
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SM contributed to conceptualization, funding acquisition, and supervision; SM and AO were involved in methodology; and AO, SM, and CK contributed to formal analysis and investigation, writing—original draft preparation, writing—review and editing, and resources.

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Correspondence to Sokratis Makrogiannnis.

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Okorie, A., Kambhamettu, C. & Makrogiannnis, S. Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping. Machine Vision and Applications 34, 103 (2023). https://doi.org/10.1007/s00138-023-01451-w

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  • DOI: https://doi.org/10.1007/s00138-023-01451-w

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