Extension and Implementation of a Model-based Approach to Hyperspectral Change Detection

被引：1
|
作者：
Meola, Joseph [1 ]
Eismann, Michael T. [1 ]
Moses, Randolph L. [2 ]
Ash, Joshua N. [2 ]
机构：
[1] USAF, Res Lab, 2241 Avion Circle, Wright Patterson AFB, OH 45433 USA
[2] Ohio State Univ, Columbus, OH 43210 USA
来源：
ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XVII | 2011年 / 8048卷
关键词：
hyperspectral; change detection; optimization; subspace models;
D O I：
10.1117/12.883265
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
A new method for hyperspectral change detection derived from a parametric radiative transfer model was recently developed. The model-based approach explicitly accounts for local illumination variations, such as shadows, which act as a constant source of false alarms in traditional change detection techniques. Here we formally derive the model-based approach as a generalized likelihood ratio test (GLRT) developed from the data model. Additionally, we discuss variations on implementation techniques for the algorithm and provide results using tower-based data and HYDICE data.
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页数：13
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