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

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:
 [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-14-29492
Grant/Contract Number:
AC52-06NA25396
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
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; learned dictionaries; sparse approximation; unsupervised classification; undercomplete dictionaries
OSTI Identifier:
1392799

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., Web. doi:10.1109/AIPR.2014.7041921.
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. doi:10.1109/AIPR.2014.7041921.
Moody, Daniela I., Brumby, Steven P., Rowland, Joel C., Altmann, Garrett L., and Larson, Amy E.. 2014. "Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries". United States. doi:10.1109/AIPR.2014.7041921. https://www.osti.gov/servlets/purl/1392799.
@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 = {2014},
month = {10}
}