Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features

Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features

Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF) instead of PCA to reduce the dimensionality o...

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Bibliographic Details
Main Authors: Guangyi Chen, Adam Krzyzak, Shen-en Qian
Format: Article
Language:English
Published: Slovenian Society for Stereology and Quantitative Image Analysis 2023-07-01
Series:Image Analysis and Stereology
Subjects:
edge preserving features (epfs)
hyperspectral image (hsi) classification
minimum noise fraction (mnf)
principal component analysis (pca)
support vector machine (svm)
Online Access:https://www.ias-iss.org/ojs/IAS/article/view/2928

Internet

https://www.ias-iss.org/ojs/IAS/article/view/2928

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