Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

Band Selection Using Independent Component Analysis for Hyperspectral Image Processing

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Indepe...

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...

Firefly Based Band Selection for Hyperspectral Image Classification

Hyperspectral Image Data Mining for Band Selection in Agricultural Applications

Hyperspectral remote sensing produces large volumes of data, quite often requiring hundreds of megabytes to gigabytes of memory storage for a small geographical area for one−time data collection. Although the high spectral resolution of hyperspectral data is quite useful for capturing and discriminating subtle differences in geospatial characteristics of the target, it contains redundant inform...

Methodology for Hyperspectral Band Selection

While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement ...