Paper
22 July 1997 Maximum-likelihood multispectral pixel-level fusion using a linear-quadratic detector
Samuel L. Earp, James M. Dang
Author Affiliations +
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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Samuel L. Earp and James M. Dang "Maximum-likelihood multispectral pixel-level fusion using a linear-quadratic detector", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); https://doi.org/10.1117/12.280856
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Cited by 1 scholarly publication.
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KEYWORDS
Sensors

Mahalanobis distance

Mining

Algorithm development

Detection and tracking algorithms

Radon

Data fusion

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