Exploring the use of neural network-based band selection on hyperspectral imagery to identify informative wavelengths for improving classifier task performance

Author

Preston Chandler DarlingFollow

Issuing Body

Mississippi State University

Advisor

John Ball

Committee Member

Stanton Price

Committee Member

Ali Gurbuz

Date of Degree

8-6-2021

Original embargo terms

Worldwide

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results.

Recommended Citation

Darling, Preston Chandler, "Exploring the use of neural network-based band selection on hyperspectral imagery to identify informative wavelengths for improving classifier task performance" (2021). Theses and Dissertations. 5261.
https://scholarsjunction.msstate.edu/td/5261