Abstract

The ability to determine optimal spectral band sets for the exploitation of multispectral and hyperspectral imagery is of great concern due to data transfer, storage, and computational constraints. This study compares the performance of three band selection techniques across a range of scenarios and image exploitation algorithms. Thresholded Divergence, a technique based on Gaussian Maximum Likelihood classification, Forward Sequential Band Selection, an iterative method based on target identification algorithms, and Spectral Basis Functions, a method independent of end-exploitation, were selected for evaluation. Each of these band selection techniques was applied to two M7 multispectral images and two HYDICE hyperspectral images. Each selected optimal spectral band set for each image was classified and assessed for classification accuracy. Comparisons between band selection techniques were made based on spectral band subset size, image exploitation algorithm, image and scene type, and input parameter set.

Library of Congress Subject Headings

Remote sensing--Data processing

Publication Date

6-1-1998

Document Type

Thesis

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Schott, John

Advisor/Committee Member

Easton, Roger

Advisor/Committee Member

Fiete, Robert

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TA1632 .L38 1998

Campus

RIT – Main Campus

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