Target detection approaches to hyperspectral image classification

Chein I. Chang, Bai Xue, Chunyan Yu

研究成果: Chapter

摘要

Detection and classification are generally considered as two different separate areas. As a matter of fact, classification can be formulated as a multiple-target problem via a hypothesis testing problem from a statistical detection theory point of view where each class is specified by a particular target under a hypothesis. Two approaches can be taken to perform hyperspectral image classification (HSIC). One is to formulate an M-class HSIC as an M-hypotheses testing problem where each class is considered as a hypothesis to be tested. The other is a binary hypothesis testing problem where the null hypothesis specified by H0 represents all classes other than the class to be classified and the alternative hypothesis specified by H1 represents a class of interest (CI) to be classified. As a result, the class to be classified is considered as a signal to be detected under H1 and all other classes are considered as noise under H0. With this interpretation, this paper presents a hypothesis testing problem formulated by HSIC which extends the statistical detection theory to statistical HSIC.

原文English
主出版物標題Advances in Hyperspectral Image Processing Techniques
發行者Wiley-Blackwell
頁面565-585
頁數21
ISBN(列印)9781119687788
DOIs
出版狀態Published - 2022 11月 11

All Science Journal Classification (ASJC) codes

  • 一般工程

指紋

深入研究「Target detection approaches to hyperspectral image classification」主題。共同形成了獨特的指紋。

引用此