期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 5, 期 2, 页码 544-554出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2012.2185822
关键词
Band selection; dimensionality reduction; hyperspectral imaging; particle swarm optimization (PSO); support vector machine (SVM); urban land cover mapping
A particle swarm optimization (PSO)-based dimensionality reduction approach is proposed to use a simple searching criterion function, called minimum estimated abundance covariance (MEAC), requiring class signatures only. It has low computational cost, and the selected bands are independent of the detector or classifiers used in the following data analysis step. With such an efficient criterion, PSO can find a global optimal solution much more efficiently, compared with other frequently used searching strategies. Its performance is evaluated by support vector machine (SVM)-based classification for urban land cover mapping. In our experiments, SVM classification accuracy using PSO-selected bands is greatly higher than using all of the original bands or dimensionality-reduced data from principal component analysis (PCA) or linear discriminant analysis (LDA). In addition, the improvement on SVM accuracy can bring out even more significant improvement in classifier fusion.
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