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Proceedings Paper

Detecting anomalous objects in hyperspectral data using segmentation
Author(s): Hila Blecher Segev; Stanley R. Rotman; Dan G. Blumberg
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Paper Abstract

In this research, our goal is to identify anomalous targets in hyperspectral and multispectral images; our sole starting information is that the targets are larger then a single pixel and their spectral signatures are different than the background. The algorithm is executed as follows: first, we use the Principal Component Analysis (PCA) transformation to find the Projecting the data into this subspace, we create a two-dimensional histogram. From the peaks of this histogram, a set of segments is determined. In comparison, we can alternatively segment our image using the well-known Kmeans approach. We then define the larger clusters to be background segments; each pixel in the image is then given a value based on the minimum "distance" from one of the segment averages. We use three different distance measures: the Mahalanobis distance, the Euclidian distance and the Spectral Angle Mapper (SAM). These dissimilarity measures are used to evaluate the pixels that are extremely different from all of the background clusters. The "anomalies" can be found by thresholding the results. We present the results of a field test using this algorithm; we have succeeded in reaching high detection rates while keeping a very low false alarm rate.

Paper Details

Date Published: 2 October 2008
PDF: 9 pages
Proc. SPIE 7113, Electro-Optical and Infrared Systems: Technology and Applications V, 711314 (2 October 2008); doi: 10.1117/12.797161
Show Author Affiliations
Hila Blecher Segev, Ben-Gurion Univ. of the Negev (Israel)
Stanley R. Rotman, Ben-Gurion Univ. of the Negev (Israel)
Dan G. Blumberg, Ben-Gurion Univ. of the Negev (Israel)

Published in SPIE Proceedings Vol. 7113:
Electro-Optical and Infrared Systems: Technology and Applications V
David A. Huckridge; Reinhard R. Ebert, Editor(s)

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