The nature of statistical learning theory
The nature of statistical learning theory
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Neural Networks - 2005 Special issue: IJCNN 2005
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Hybrid particle swarm optimization algorithm with fine tuning operators
International Journal of Bio-Inspired Computation
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
Bio-inspired computing: constituents and challenges
International Journal of Bio-Inspired Computation
A novel hybrid particle swarm optimisation method applied to economic dispatch
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Improved strategy of particle swarm optimisation algorithm for reactive power optimisation
International Journal of Bio-Inspired Computation
Quickly obtaining degree of polarisation ellipsoid by using particle swarm optimisation
International Journal of Bio-Inspired Computation
Particle swarm optimisation based Diophantine equation solver
International Journal of Bio-Inspired Computation
Feature selection with Intelligent Dynamic Swarm and Rough Set
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.