BAYESIAN NON-PARAMETRIC DETECTOR BASED ON THE REPLACEMENT MODEL

被引：2

作者：

Matteoli, Stefania ^{[1
]}

Diani, Marco ^{[2
]}

Corsini, Giovanni ^{[3
]}

机构：

[1] CNR, Inst Elect Comp & Telecommun Engn IEIIT, Via G Caruso 16, I-56122 Pisa, Italy

[2] Italian Naval Acad, Viale Italia 72, I-57127 Livorno, Italy

[3] Univ Pisa, Dept Informat Engn, Via G Caruso 16, I-56122 Pisa, Italy

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

Hyperspectral; Target Detection; Bayes; non-parametric; replacement model;

D O I：

10.1109/IGARSS46834.2022.9883554

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

A Bayesian Likelihood Ratio Test (LRT) detector is analytically derived here for the replacement target model and using the non-parametric variable-bandwidth kernel density estimator to model the hyperspectral background. The detector is compared to the recent Generalized LRT detector, based on the same non-parametric model for the background. Experimental results obtained on two hyperspectral sub-pixel target detection scenarios reveal the great potential of the proposed detector and set the basis for future investigations.

引用

页码：871 / 874

页数：4

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