期刊名称：International Journal of Electronics, Communication and Soft Computing Science and Engineering
印刷版ISSN：2277-9477
出版年度：2015
卷号：4
期号：Special 2
出版社：IJECSCSE
摘要：Dimensionality reduction is a major task in remote sensing images. Feature selection is applied for performing dimensionality reduction. It selects the spectral features(i.e. Bands) and find a feature subset that preserves the semantics of the hyperspectral image. Based on particle swarm optimization (PSO), this paper proposes multi-objective functions for selecting the spectral feature subsets for classification. The multi-objective function select feature subsets based on Jeffries Matusita(JM) distance and classifier(i.e. SVM). This paper performs optimal band selection and dimensionality reduction of hyperspectral imagery . The goal of the system is to perform spectral feature selection using particle swarm optimization (PSO) based multi-objective function. The system implements multi-objective functions which performs spectral feature selection (i.e. most informative bands) from the hyperspectral image dataset. These selected features are further used for evaluating the overall classification accuracy
