Target detection in a structured background environment using an infeasibility metric in an invariant space

Show simple item record

dc.contributor.author Ientilucci, Emmett en_US
dc.contributor.author Schott, John en_US
dc.date.accessioned 2007-06-22T15:08:45Z en_US
dc.date.available 2007-06-22T15:08:45Z en_US
dc.date.issued 2005-06 en_US
dc.identifier.citation Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI 5806 (2005) 491-502 en_US
dc.identifier.issn 0277-786X en_US
dc.identifier.uri http://hdl.handle.net/1850/4059 en_US
dc.description "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. en_US
dc.description.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. en_US
dc.description.sponsorship 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. en_US
dc.language.iso en_US en_US
dc.publisher The International Society for Optical Engineering (SPIE) en_US
dc.relation.ispartofseries vol. 5806 en_US
dc.subject Hyperspectral en_US
dc.subject Infeasibility en_US
dc.subject Invariant subspace en_US
dc.subject Matched filter en_US
dc.subject Physics based modeling en_US
dc.subject Target detection en_US
dc.title Target detection in a structured background environment using an infeasibility metric in an invariant space en_US
dc.type Article en_US

Files in this item

Files Size Format View
JSchottConfProc06-2005.pdf 1.298Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search RIT DML


Advanced Search

Browse