An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification

Autor:	Guangyi Chen, Adam Krzyzak, Shen-en Qian
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	edge preserving features hyperspectral image classification minimum noise fraction principal component analyses support vector machine Medicine (General) R5-920 Mathematics QA1-939
Zdroj:	Image Analysis and Stereology, Vol 43, Iss 2 (2024)
Druh dokumentu:	article
ISSN:	1580-3139 1854-5165
DOI:	10.5566/ias.3078
Popis:	Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In this paper, we conduct a systematic study to examine the effects of noise in HSI data cubes on classification methods. We compare ten existing methods for HSI classification when Gaussian white noise (GWN) and shot noise are present in the HSI data cubes. We have figured out which method is the most robust to GWN and shot noise respectively by experimenting on three widely used HSI data cubes. We have also measured the CPU computational time of every method compared in this paper for HSI classification.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f83187dd1ea6461dba1365e7293d438a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF