Name: Particle Swarm Optimization-Based Hyperspectral Dimensionality Reduction for Urban Land Cover Classi?cation
SKU: PROJ5818
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Particle Swarm Optimization-Based Hyperspectral Dimensionality Reduction for Urban Land Cover Classi?cation

Abstract? Particle Swarm Optimization-Based Hyperspectral Dimensionality Reduction for Urban Land Cover Classi?cation. A particle swarm optimization < Final Year Projects 2016 > 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 classi?ers used in the following data analysis step. With such an ef?cient criterion, PSO can ?nd a global optimal solution much more ef?ciently, compared with other frequently used searching strategies. Its performance is evaluated by support vector machine (SVM)-based classi?cation for urban land cover mapping. In our experiments, SVM classi?cation 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 signi?cant improvement in classi?er fusion.

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