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Probabilistic Topic Modeling for Hyp...

Boggs, Thomas P.

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Probabilistic Topic Modeling for Hyperspectral Image Classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Probabilistic Topic Modeling for Hyperspectral Image Classification./
Author:	Boggs, Thomas P.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	194 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13877849
ISBN:	9781392230282

Probabilistic Topic Modeling for Hyperspectral Image Classification.
Boggs, Thomas P.

Probabilistic Topic Modeling for Hyperspectral Image Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 194 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--George Mason University, 2019.

This item must not be sold to any third party vendors.

Probabilistic Topic Models are a family of mathematical models used primarily to identify topics in large collections of text documents. This research adapts the topic modeling approach to the unsupervised classification of hyperspectral images. By considering image pixels similarly to text documents and quantizing data for each spectral band to develop a spectral feature vocabulary, it is demonstrated that by using Latent Dirichlet Allocation with a hyperspectral image corpus, learned topics can be used to produce unsupervised classification results that often match ground truth better than the commonly used k-means algorithm. The topic modeling approach developed is demonstrated to easily extend to classification of image regions by aggregating spectral features over spatial windows. The region-based document models are shown to account for the spectral covariance and heterogeneity of ground-cover classes, resulting in similarity to land use ground truth that increases monotonically with window size.Multiresolution wavelet decompositions of pixel reflectance spectra are used to develop a novel feature vocabulary that more naturally aligns with material absorption and reflectance features, further improving classification results. The wavelet-based document modeling approach is evaluated against synthetic image data, a small AVIRIS image with 16 groundtruth classes, and finally on practical-sized, overlapping AVIRIS and Hyperion images to demonstrate the utility of the models. Multiple wavelet bases and numbers of quantization levels are considered and for the data sets evaluated, it is determined that using the Haar wavelet with 10 quantization levels yields the best performance, while also producing easily interpretable topics. It is demonstrated that by omitting low-level wavelet coefficients, vocabulary size and model inference time can be significantly reduced without loss of accuracy.The wavelet-based approach is extended by replacing quantization levels with simple thresholds for positive and negative wavelet coefficients, reducing the vocabulary size to two times the number of wavelet coefficients. The thresholded wavelet model provides accuracy comparable to the quantized wavelet model, while having significantly shorter inference time and supporting easily interpretable visualization of topics in the wavelet domain. By establishing appropriate model hyperparameters and omitting low-level wavelet coefficients, the thresholded wavelet model provides better unsupervised classification results than previously developed quantized band models, has shorter model parameter estimation time, and has an average document word count smaller by a factor of 5 and a vocabulary smaller by a factor of 10.

ISBN: 9781392230282Subjects--Topical Terms:

517247
Statistics.

Probabilistic Topic Modeling for Hyperspectral Image Classification.
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300 $a 194 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
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502 $a Thesis (Ph.D.)--George Mason University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Probabilistic Topic Models are a family of mathematical models used primarily to identify topics in large collections of text documents. This research adapts the topic modeling approach to the unsupervised classification of hyperspectral images. By considering image pixels similarly to text documents and quantizing data for each spectral band to develop a spectral feature vocabulary, it is demonstrated that by using Latent Dirichlet Allocation with a hyperspectral image corpus, learned topics can be used to produce unsupervised classification results that often match ground truth better than the commonly used k-means algorithm. The topic modeling approach developed is demonstrated to easily extend to classification of image regions by aggregating spectral features over spatial windows. The region-based document models are shown to account for the spectral covariance and heterogeneity of ground-cover classes, resulting in similarity to land use ground truth that increases monotonically with window size.Multiresolution wavelet decompositions of pixel reflectance spectra are used to develop a novel feature vocabulary that more naturally aligns with material absorption and reflectance features, further improving classification results. The wavelet-based document modeling approach is evaluated against synthetic image data, a small AVIRIS image with 16 groundtruth classes, and finally on practical-sized, overlapping AVIRIS and Hyperion images to demonstrate the utility of the models. Multiple wavelet bases and numbers of quantization levels are considered and for the data sets evaluated, it is determined that using the Haar wavelet with 10 quantization levels yields the best performance, while also producing easily interpretable topics. It is demonstrated that by omitting low-level wavelet coefficients, vocabulary size and model inference time can be significantly reduced without loss of accuracy.The wavelet-based approach is extended by replacing quantization levels with simple thresholds for positive and negative wavelet coefficients, reducing the vocabulary size to two times the number of wavelet coefficients. The thresholded wavelet model provides accuracy comparable to the quantized wavelet model, while having significantly shorter inference time and supporting easily interpretable visualization of topics in the wavelet domain. By establishing appropriate model hyperparameters and omitting low-level wavelet coefficients, the thresholded wavelet model provides better unsupervised classification results than previously developed quantized band models, has shorter model parameter estimation time, and has an average document word count smaller by a factor of 5 and a vocabulary smaller by a factor of 10.
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