Application of multi-scale singular vector decomposition to vessel classification in overhead satellite imagery

R. Izmailov; D. Bassu; A. McIntosh; L. Ness; D. Shallcross

doi:10.1117/12.2196925

6 July 2015 Application of multi-scale singular vector decomposition to vessel classification in overhead satellite imagery

R. Izmailov, D. Bassu, A. McIntosh, L. Ness, D. Shallcross

Author Affiliations +

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 963108 (2015) https://doi.org/10.1117/12.2196925
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

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.

Citation Download Citation

R. Izmailov, D. Bassu, A. McIntosh, L. Ness, and D. Shallcross "Application of multi-scale singular vector decomposition to vessel classification in overhead satellite imagery ", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963108 (6 July 2015); https://doi.org/10.1117/12.2196925

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