Supervised classification of civil air patrol (CAP)

Elena Sava (), Laura Clemente-Harding and Guido Cervone
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Elena Sava: The Pennsylvania State University
Laura Clemente-Harding: The Pennsylvania State University
Guido Cervone: The Pennsylvania State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 86, issue 2, No 3, 535-556

Abstract: Abstract The mitigation and response to floods rely on accurate and timely flood assessment. Remote sensing technologies have become the de facto approach for observing the Earth and its environment. However, satellite remote sensing data are not always available, and it is crucial to develop new techniques to complement them with additional sources. This research proposes a new methodology based on machine learning algorithms to automatically identify water pixels in Civil Air Patrol (CAP) aerial imagery. Specifically, a wavelet transformation is paired with multiple classifiers to build models that discriminate water and non-water pixels. The learned classification models are first tested against a set of control cases and then used to automatically classify each image separately. Lastly, for each pixel in an image, a measure of uncertainty is computed as a proportion of the number of models that classify the pixel as water. The proposed methodology is tested on imagery collected during the 2013 Colorado flood.

Keywords: Supervised classification; Remote sensing; Damage assessment; Natural hazards; Flood; Civil air patrol (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-016-2704-3

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