June 16, 2021
Faculty and Students,
You are cordially invited to my thesis defense.
Title: Exploring the Use of Neural Network-Based Band Selection on Hyperspectral Imagery to Identify Informative Wavelengths for Improving Classifier Task Performance
When: Tuesday, June 22nd at 1:00 p.m.
Candidate: Preston Darling
Degree: Masters, Electrical and Computer Engineering
Dr. John Ball
(Major Professor )
Dr. Ali Gurbuz
Dr. Stanton Price
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.
Keywords: Remote Sensing, Machine Learning, Hyperspectral Imaging, Band Selection