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Title: Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

Abstract

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Inventors:
Issue Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1435634
Patent Number(s):
9946931
Application Number:
15/133,387
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 2016 Apr 20
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Moody, Daniela Irina. Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery. United States: N. p., 2018. Web.
Moody, Daniela Irina. Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery. United States.
Moody, Daniela Irina. Tue . "Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery". United States. https://www.osti.gov/servlets/purl/1435634.
@article{osti_1435634,
title = {Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery},
author = {Moody, Daniela Irina},
abstractNote = {An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {4}
}

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