Paper Info
Title
Application of multi-scale singular vector decomposition to vessel classification in overhead satellite imagery
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.
Year
DOI
Venue
2015
10.1117/12.2196925
Proceedings of SPIE
Keywords
Field
DocType
Multiscale,singular value decomposition,machine learning,feature construction,SVM
Data mining,Singular value decomposition,Satellite imagery,Classification rule,Pattern recognition,Domain knowledge,Vector decomposition,Support vector machine,Pixel,Artificial intelligence,Contextual image classification,Mathematics
Conference
Volume
ISSN
Citations 
9631
0277-786X
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Rauf Izmailov120036.96
Devasis Bassu22265.13
anthony r mcintosh300.34
l ness400.68
david f shallcross500.34