Abstract

This paper develops a hybrid target detector that incorporates structured backgrounds and physics based modeling together with a geometric infeasibility metric. More often than not, detection algorithms are usually applied to atmospherically compensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations of what the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented as applied to the generation of target spaces. The generated target spaces are designed to fully encompass image target pixels while using a limited number of input model parameters. Background spaces are modeled using a linear subspace (structured) approach characterized by endmembers found by using the maximum distance method (MaxD). After augmenting the image data with the target space, 15 endmembers were found, which were not related to the target (i.e., background endmembers). A geometric infeasibility metric is developed which enables one to be more selective in rejecting false alarms. Preliminary results in the design of such a metric show that an orthogonal projection operator based on target space vectors can distinguish between target and background pixels. Furthermore, when used in conjunction with an operator that produces abundance-like values, we obtained separation between target, ackground, and anomalous pixels. This approach was applied to HYDICE image spectrometer data.

Publication Date

2005

Comments

"Target detection in a structured background environment using an infeasibility metric in an invariant space," Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE volume 5806. Held in Orlando, Florida: March 2005. Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The author would like to thank David Messinger and Professor John Kerekes for suggestions regarding this paper. This work was funded under the Office of Naval Research Multi-disciplinary University Research Initiative “Model-based Hyperspectral Exploitation Algorithm Development” #N00014-01-1-0867.ISSN:0277-786X Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Article

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Campus

RIT – Main Campus

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