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Proceedings Paper

Building robust neighborhoods for manifold learning-based image classification and anomaly detection
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Paper Abstract

We exploit manifold learning algorithms to perform image classification and anomaly detection in complex scenes involving hyperspectral land cover and broadband IR maritime data. The results of standard manifold learning techniques are improved by including spatial information. This is accomplished by creating super-pixels which are robust to affine transformations inherent in natural scenes. We utilize techniques from harmonic analysis and image processing, namely, rotation, skew, flip, and shift operators to develop a more representational graph structure which defines the data-dependent manifold.

Paper Details

Date Published: 25 May 2016
PDF: 13 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 984015 (25 May 2016); doi: 10.1117/12.2227224
Show Author Affiliations
Timothy Doster, U.S. Naval Research Lab. (United States)
Colin C. Olson, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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