Hyperspectral Image Classification Methods

Lu Jiang, Bin Zhu, Yang Tao

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter introduces several feature selection and pattern recognition methods that are often used in hyperspectral imagery. Distance metrics and feature search strategies are two main aspects in the feature selection. The goal of linear projection-based feature selection methods is to transform the image data from original space into another space of a lower dimension. A second-order statistics-based classification method needs the assumption of a probability density model of the data, and such an assumption itself is a challenging problem. Neural networks are nonlinear statistical data modeling tools, which can be used to model complex relationships between inputs and outputs in order to find patterns in the image data. The kernel method appears to be especially advantageous in the analysis of hyperspectral data. For example, support vector machine implements a maximum margin-based geological classification strategy, which shows the robustness of high dimensionality of the hyperspectral data and low sensitivity of the number of training data.

Original languageEnglish (US)
Title of host publicationHyperspectral Imaging for Food Quality Analysis and Control
PublisherElsevier Inc.
Pages79-98
Number of pages20
ISBN (Print)9780123747532
DOIs
StatePublished - Dec 1 2010

ASJC Scopus subject areas

  • Chemistry(all)

Fingerprint Dive into the research topics of 'Hyperspectral Image Classification Methods'. Together they form a unique fingerprint.

  • Cite this

    Jiang, L., Zhu, B., & Tao, Y. (2010). Hyperspectral Image Classification Methods. In Hyperspectral Imaging for Food Quality Analysis and Control (pp. 79-98). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-374753-2.10003-6