Dissertation Announcement for Chiranjibi Shah — 12/06/2021 at 2:00pm
November 23, 2021
Dear Faculty, Graduate, and Undergraduate Students,
You are cordially invited to my Ph.D. dissertation defense.
Dissertation title: Spatial-spectral Analysis in Dimensionality Reduction for Hyperspectral Image Classification
When: Monday, December 6, 2021, 2:00 pm
Where: Simrall 228
Webex link for online access:
https://msstate.webex.com/msstate/j.php?MTID=m09f87c8c7361b81523a8965d34cbc4c3
Candidate: Chiranjibi Shah
Degree: Ph.D., Electrical and Computer Engineering
Committee:
Dr. Qian Du
Professor of Electrical and Computer Engineering
(Major Professor)
Dr. Nicolas H. Younan
Professor and Department Head Emeritus of Electrical and Computer Engineering
(Committee Member)
Dr. James E. Fowler
Professor of Electrical and Computer Engineering
(committee Member)
Dr. Bo Tang
Assistant Professor of Electrical and Computer Engineering
(Committee Member)
Abstract:
This dissertation develops new algorithms with different techniques in utilizing spatial and spectral information for hyperspectral image classification. It is necessary to perform spatial and spectral analysis and conduct dimensionality reduction (DR) for effective feature extraction, because hyperspectral imagery consists of a large number of spatial pixels along with hundreds of spectral dimensions.
In the first proposed method, it employs spatial-aware collaboration-competition preserving graph embedding by imposing a spatial regularization term along with Tikhonov regularization in the objective function for DR of hyperspectral imagery. Moreover, Collaboration representation (CR) is an efficient classifier but without using spatial information. Thus, structure-aware collaborative representation (SaCRT) is introduced to utilize spatial information for more appropriate data representations. It is demonstrated that better classification performance can be offered by the SaCRT in this work.
For DR, collaborative and low-rank representation-based graph for discriminant analysis of hyperspectral imagery is proposed. It can generate a more informative graph by combining collaborative and low-rank representation terms. With the collaborative term, it can incorporate within-class atoms. Meanwhile, it can preserve global data structure by use of the low-rank term. Since it employs a collaborative term in the estimation of representation coefficients, its closed-form solution results in less computational complexity in comparison to sparse representation. The proposed collaborative and low-rank representation-based graph can outperform the existing sparse and low-rank representation-based graph for DR of hyperspectral imagery.
The concept of tree-based techniques and deep neural networks can be combined by use of an interpretable canonical deep tabular data learning architecture (TabNet). It uses sequential attention for choosing appropriate features at different decision steps. An efficient TabNet for hyperspectral image classification is developed in this dissertation, in which the performance of TabNet is enhanced by incorporating a 2-D convolution layer inside an attentive transformer. Additionally, better classification performance of TabNet can be obtained by utilizing structure profiles on TabNet.
Sincerely,
Shah, Chiranjibi