PARTICLE SWARM OPTIMIZATION-BASED DIMENSIONALITY REDUCTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

We propose a particle swarm optimization (PSO)-based dimensionality reduction approach to improve support vector machine (SVM)-based classification for high-resolution hyperspectral imagery. After a searching criterion function is well designed, PSO can find a global optimal solution much more efficiently, compared to other frequently used searching strategies. In our experiments, SVM classification accuracy using PSO-selected bands is greatly higher than using all the original bands or dimensionality-reduced data from principal component analysis (PCA) or linear discriminant analysis (LDA). In addition, misclassification incurred from trivial within-class spectral variation can be further corrected by decision fusion with an unsupervised clustering, where the improvement on SVM accuracy can bring out even more significant improvement in the final fusion output.