SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES

Band Subset Selection for Hyperspectral Image Classification

This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so...

Hyperspectral band clustering and band selection for urban land cover classification

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Firefly Based Band Selection for Hyperspectral Image Classification

Band Selection with CFI and Supervised Classification for Hyperspectral Images

In this paper, we propose a new feature selection method for hyperspectral images. Firstly, the bands are selected by combining the information entropy, classification separability and correlation coefficients with the Choquet fuzzy integral. After that, maximum likelihood classification method is used for the classification. Experiments on the AVIRIS dataset show that the proposed method remov...

Methodology for Hyperspectral Band and Classification Model Selection

Feature selection is one of the fundamental problems in nearly every application of statistical modeling, and hyperspectral data analysis is no exception. We propose a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement constraints. It is designed to perform not only hyperspectral band (wavelength range) selection but als...