Date of Award:
5-2014
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Electrical and Computer Engineering
Committee Chair(s)
Jacob Gunther
Committee
Jacob Gunther
Committee
Todd Moon
Committee
Adele Cutler
Abstract
Hyperspectral imaging (HSI) produces spatial images with pixels that, instead of consisting of three colors, consist of hundreds of spectral measurements. Because there are so many measurements for each pixel, analysis of HSI is difficult. Frequently, standard techniques are used to help make analysis more tractable by representing the HSI data in a different manner.
This research explores the utility of representing the HSI data in a learned dictionary basis for the express purpose of material identification and classification. Multiclass classification is performed on the transformed data using the RandomForests algorithm. Performance results are reported.
In addition to classification, single material detection is considered also. Commonly used detection algorithm performance is demonstrated on both raw radiance pixels and HSI represented in dictionary-learned bases. Comparison results are shown which indicate that detection on dictionary-learned sparse representations perform as well as detection on radiance. In addition, a different method of performing detection, capitalizing on dictionary learning is established and performance comparisons are reported, showing gains over traditional detection methods.
Checksum
55e1a2b6dd08b3bf0e311db882b0ff7b
Recommended Citation
Pound, Andrew E., "Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery" (2014). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 4020.
https://digitalcommons.usu.edu/etd/4020
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