Abstract:Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then… Show more
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