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 |
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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