Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; Lawrence, Rick L.; Powell, Scott

doi:10.1109/JSTARS.2017.2701360

Title: Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

Abstract

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splitting of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this papermore »« less

Authors:

McCann, Cooper ^[1]; Repasky, Kevin S. ^[1]; Morin, Mikindra ^[1]; Lawrence, Rick L. ^[1]; Powell, Scott ^[1]

Montana State Univ., Bozeman, MT (United States)

Publication Date:: Tue May 23 00:00:00 EDT 2017

Research Org.:: Montana State Univ., Bozeman, MT (United States)

Sponsoring Org.:: USDOE Office of Fossil Energy (FE)

OSTI Identifier:: 1409671

Grant/Contract Number:: FC26-05NT42587

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Additional Journal Information:: Journal Volume: 10; Journal Issue: 9; Journal ID: ISSN 1939-1404

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; histograms; vegetation mapping; training; hyperspectral imaging; data models; sensors; agriculture; biophysics; clustering methods; remote sensing

Citation Formats


                    McCann, Cooper, Repasky, Kevin S., Morin, Mikindra, Lawrence, Rick L., and Powell, Scott. Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data.  United States: N. p., 2017. 
Web.  doi:10.1109/JSTARS.2017.2701360.

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                    McCann, Cooper, Repasky, Kevin S., Morin, Mikindra, Lawrence, Rick L., & Powell, Scott. Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data.  United States.  https://doi.org/10.1109/JSTARS.2017.2701360

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                    McCann, Cooper, Repasky, Kevin S., Morin, Mikindra, Lawrence, Rick L., and Powell, Scott. Tue .  
"Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data".  United States.  https://doi.org/10.1109/JSTARS.2017.2701360.  https://www.osti.gov/servlets/purl/1409671.

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@article{osti_1409671,

  title        = {Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data},

  author       = {McCann, Cooper and Repasky, Kevin S. and Morin, Mikindra and Lawrence, Rick L. and Powell, Scott},

  abstractNote = {Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splitting of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.},

  doi          = {10.1109/JSTARS.2017.2701360},

  journal      = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

  number       = 9,

  volume       = 10,

  place        = {United States},

  year         = {Tue May 23 00:00:00 EDT 2017},

  month        = {Tue May 23 00:00:00 EDT 2017}

}

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https://doi.org/10.1109/JSTARS.2017.2701360

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Works referenced in this record:

220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3
dataset, January 2015

Baumgardner, Marion; Biehl, Larry; Landgrebe, David
Purdue University Research Repository
DOI: 10.4231/r7rx991c

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Title: Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

Abstract

Citation Formats

220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3 dataset, January 2015

220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3
dataset, January 2015