Research Papers

Extending classification approaches to hyperspectral object detection

[+] Author Affiliations
Rulon Mayer

BAE Systems-Advanced Technologies

John Antoniades, Mark Baumback, David Chester, Jonathan Edwards, Alon Goldstein, Dan Haas, Sam Henderson

BAE Systems

J. Appl. Remote Sens. 1(1), 013526 (August 7, 2007). doi:10.1117/1.2776954
History: Received February 9, 2007; Revised June 20, 2007; Accepted August 1, 2007; August 7, 2007; Online August 07, 2007
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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.

© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Rulon Mayer ; John Antoniades ; Mark Baumback ; David Chester ; Jonathan Edwards, et al.
"Extending classification approaches to hyperspectral object detection", J. Appl. Remote Sens. 1(1), 013526 (August 7, 2007). ; http://dx.doi.org/10.1117/1.2776954


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Adaptive Nonlocal Sparse Representation for Dual-Camera Compressive Hyperspectral Imaging. IEEE Trans Pattern Anal Mach Intell Published online Oct 25, 2016;
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