Spectral and spatial feature classification of hyperspectral images based on particle swarm optimisation. (1st January 2012)

• Record Type: Journal Article
• Authors: Ding, Sheng; Chen, Li
• Is Part Of: International journal of innovative computing and applications. Volume 4:Number 3/4(2012)
• Page Start: 233
• Page End: 242
• Publication Date: 2012-01-01
• Subjects: support vector machine -- SVM -- particle swarm optimisation -- PSO -- optimisation -- feature selection -- grey level co-occurrence matrix -- GLCM; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

This paper addresses the problem of feature selection and SVM kernel parameter optimisation for hyperspectral remote sensing image. First, we propose an evolutionary classification algorithm based on particle swarm optimisation (PSO) to improve the generalisation performance of the SVM classifier. For this purpose, we have optimised the SVM classifier design by searching for the best value of the kernel parameters of SVM that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Second, we propose a feature fusion approach based on the joint use of spectral and spatial information provided by texture features extracted from the grey level co-occurrence matrix (GLCM). Experimental results prove that spectral feature and GLCM texture features can obtain higher classification accuracy than only spectral feature classification for hyperspectral image classification.