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Proceedings Paper

Maximum-likelihood multispectral pixel-level fusion using a linear-quadratic detector
Author(s): Samuel L. Earp; James M. Dang
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Paper Abstract

Past work in multispectral data fusion has focused on either ad-hoc techniques or conventional variants of the multivariate Gaussian model. This work develops a generalized likelihood ratio for pixel-level fusion that utilizes an unconventional region of support for the unknown target signature. The intent is to demonstrate that partial knowledge of target signature or clutter characteristics can be effectively captured and utilized in a fusion algorithm based on this likelihood ratio. The choice of model was motivated by the need to account for the variability of the multispectral target signatures; in essence, the target signature is modeled to be known within a band of spectral intensities. The resulting estimators were determined initially to be computationally complex, growing in complexity at an exponential rate with the number of bands to be fused. One result of the work was that an algorithm was created that kept the complexity cubic with respect to the number of spectral bands. Another intriguing result was that classic linear and quadrati detectors are special cases of the generalized likelihood ratio; the optimum detector structure for orthant support sets is a linear-quadratic detector, where the subsets of bands to be processed in each mode are in effect chosen on-the-fly. Additional results and examples will be presented.

Paper Details

Date Published: 22 July 1997
PDF: 17 pages
Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280856
Show Author Affiliations
Samuel L. Earp, MITRE Corp. (United States)
James M. Dang, MITRE Corp. (United States)


Published in SPIE Proceedings Vol. 3079:
Detection and Remediation Technologies for Mines and Minelike Targets II
Abinash C. Dubey; Robert L. Barnard, Editor(s)

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