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Proceedings Paper

Application of multi-scale singular vector decomposition to vessel classification in overhead satellite imagery
Author(s): R. Izmailov; D. Bassu; A. McIntosh; L. Ness; D. Shallcross
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Paper Abstract

Creation and selection of relevant features for image classification is a process requiring significant involvement of domain knowledge. It is thus desirable to cover at least part of that process with semi-automated techniques capable of discovering and visualizing those geometric characteristics of images that are potentially relevant to the classification objective. In this work, we propose utilizing the multi-scale singular value decomposition (MSVD), which can be efficiently run on large high-dimensional datasets. We apply this technique to create a multi-scale representation of overhead satellite images of various types of vessels, with the objective of identifying those types. We augment the original set of pixel data with features obtained by applying the MSVD to multi-scale patches of the images. The result is then processed using a linear Support Vector Machine (SVM) algorithm. The classification rule obtained is significantly better than the one based on the original pixel space. The generic nature of the MSVD mechanism and standard mechanisms used for classification (SVM) suggest a wider utility of the proposed approach.

Paper Details

Date Published: 6 July 2015
PDF: 6 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963108 (6 July 2015); doi: 10.1117/12.2196925
Show Author Affiliations
R. Izmailov, Applied Communication Sciences (United States)
D. Bassu, Applied Communication Sciences (United States)
A. McIntosh, Applied Communication Sciences (United States)
L. Ness, Applied Communication Sciences (United States)
D. Shallcross, Applied Communication Sciences (United States)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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