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Proceedings Paper

Interference-invariant target detection in hyperspectral images
Author(s): Terry L. Nichols; John K. Thomas; Wolfgang Kober; Vincent J. Velten
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Paper Abstract

In this paper we address the problem of detecting targets in hyperspectral images when the target signature is buried in random noise and interference (from other materials in the same pixel). We assume that the hyperspectral pixel measurement is a linear combination of the target and interference signatures observed in additive noise. The linear mixing assumption leads to a linear vector space interpretation of the measurement vector, which can be decomposed into a noise-only subspace and a target-plus- interference subspace. While it is true that the target and interference subspaces are orthogonal to the noise-only subspace, the target subspace and interference subspace are, in general, not orthogonal. The non-orthogonality between the target and interference subspaces results in leakage of interference signals into the output of matched filters resulting in false detections (i.e., higher false alarm rates). In this paper, we replace the Matched Filer Detector (MFD), which is based on orthogonal projections, with a Matched Subspace Detector (MSD), which is built on non- orthogonal or oblique projections. The advantage of oblique projections is that they eliminate the leakage of interference signals into the detector, thereby making detectors based on oblique projections invariant to the amount of interference. Furthermore, under Gaussian assumptions for the additive noise, it has been shown that the MSD is Uniformly Most Powerful (higher probability of detect for a fixed probability of false alarm) among all detectors that share this invariance to interference power. In this paper we evaluate the ability of two versions of the MSD to detect targets in HYDICE data collected over sites A and B located at the U.S. Army Yuma proving grounds. We compute data derived receiver operating characteristics (ROC) curves and show that the MSD out- performs the MFD.

Paper Details

Date Published: 2 July 1998
PDF: 12 pages
Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312598
Show Author Affiliations
Terry L. Nichols, Data Fusion Corp. (United States)
John K. Thomas, Data Fusion Corp. (United States)
Wolfgang Kober, Data Fusion Corp. (United States)
Vincent J. Velten, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3372:
Algorithms for Multispectral and Hyperspectral Imagery IV
Sylvia S. Shen; Michael R. Descour, Editor(s)

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