Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.; Larson, Amy E.

doi:10.1109/AIPR.2014.7041921

Title: Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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Abstract

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.

Authors:

Moody, Daniela I. ^[1]; Brumby, Steven P. ^[1]; Rowland, Joel C. ^[1]; Altmann, Garrett L. ^[1]; Larson, Amy E. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: Wed Oct 01 00:00:00 EDT 2014

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1392799

Report Number(s):: LA-UR-14-29492

Grant/Contract Number:: AC52-06NA25396

Resource Type:: Accepted Manuscript

Journal Name:: IEEE AIPR Conference proceedings

Additional Journal Information:: Conference: 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC (United States), 14-16 Oct 2014; Related Information: ISBN: 978-1-4799-5921-1

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; learned dictionaries; sparse approximation; unsupervised classification; undercomplete dictionaries

Citation Formats


                    Moody, Daniela I., Brumby, Steven P., Rowland, Joel C., Altmann, Garrett L., and Larson, Amy E. Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries.  United States: N. p., 2014. 
Web.  doi:10.1109/AIPR.2014.7041921.

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                    Moody, Daniela I., Brumby, Steven P., Rowland, Joel C., Altmann, Garrett L., & Larson, Amy E. Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries.  United States.  https://doi.org/10.1109/AIPR.2014.7041921

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                    Moody, Daniela I., Brumby, Steven P., Rowland, Joel C., Altmann, Garrett L., and Larson, Amy E. Wed .  
"Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries".  United States.  https://doi.org/10.1109/AIPR.2014.7041921.  https://www.osti.gov/servlets/purl/1392799.

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@article{osti_1392799,

  title        = {Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries},

  author       = {Moody, Daniela I. and Brumby, Steven P. and Rowland, Joel C. and Altmann, Garrett L. and Larson, Amy E.},

  abstractNote = {Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.},

  doi          = {10.1109/AIPR.2014.7041921},

  journal      = {IEEE AIPR Conference proceedings},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Oct 01 00:00:00 EDT 2014},

  month        = {Wed Oct 01 00:00:00 EDT 2014}

}

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