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Journal of Applied Remote Sensing

Extending classification approaches to hyperspectral object detection
Author(s): Rulon R. Mayer; John A. Antoniades; Mark M. Baumback; David Chester; Jonathan Edwards; Alon Goldstein; Daniel Haas; Samuel Henderson
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Paper Abstract

This study adapts a variety of multi-spectral image classification techniques to generate supervised object detection algorithms for hyperspectral imagery, and compares and quantitatively tests them against the Adaptive Cosine Estimator (ACE) and the standard, matched filter (MF). A new search algorithm, Regularized Maximum Likelihood Clustering (RMLC), uses only pixels for the covariance matrix (CV) computation associated with the object after "regularizing" the matrix to avoid singularities and mitigate statistical degradation due to undersampling for small objects. The searches are applied to both visible/near IR and short wave IR data collected from forested areas. This study tests the detection sensitivity by using object signatures and CVs taken directly from the scene and from temporally transformed signatures and object CVs. This study adds simple, high performing algorithms to the small object search arsenal.

Paper Details

Date Published: 1 August 2007
PDF: 12 pages
J. Appl. Remote Sens. 1(1) 013526 doi: 10.1117/1.2776954
Published in: Journal of Applied Remote Sensing Volume 1, Issue 1
Show Author Affiliations
Rulon R. Mayer, BAE Systems Advanced Information Technologies (United States)
John A. Antoniades, BAE Systems Advanced Information Technologies (United States)
Mark M. Baumback, BAE Systems Advanced Information Technologies (United States)
David Chester, BAE Systems Advanced Information Technologies (United States)
Jonathan Edwards, BAE Systems Advanced Information Technologies (United States)
Alon Goldstein, BAE Systems Advanced Information Technologies (United States)
Daniel Haas, BAE Systems Advanced Information Technologies (United States)
Samuel Henderson, BAE Systems Advanced Information Technologies (United States)


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