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

Moody, Daniela Irina

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:: Moody, Daniela Irina

Issue Date:: 2018-04-17

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

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G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
G06K9/6218 - {Clustering techniques}
G06K9/00536 - {Classification
G06K9/0063 - {Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas

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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. 
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                    Moody, Daniela Irina. Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery.  United States.

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                    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.

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@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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Similar Records

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

Abstract

Citation Formats

Dictionary Learning with Large Step Gradient Descent for Sparse Representations book, January 2012

Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities journal, August 2000

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers book, January 2011

A Sherman–Morrison approach to the solution of linear systems journal, May 2006

From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images journal, February 2009

Task-Driven Dictionary Learning journal, April 2012

An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems journal, March 2011

Atomic Decomposition by Basis Pursuit journal, January 1998

A fast proximal method for convolutional sparse coding conference, August 2013

Explicit Shift-Invariant Dictionary Learning journal, January 2014

Updating the Inverse of a Matrix journal, June 1989

Method of optimal directions for frame design conference, January 1999

Fast Image Recovery Using Variable Splitting and Constrained Optimization journal, September 2010

Learning mid-level features for recognition conference, June 2010

On Shift-Invariant Sparse Coding book, January 2004

Image compression using learned dictionaries by RLS-DLA and compared with K-SVD conference, May 2011

Fast Convolutional Sparse Coding conference, June 2013

Non-local sparse models for image restoration conference, September 2009

Unsupervised learning of sparse and shift-invariant decompositions of polyphonic music conference, January 2004

Supervised translation-invariant sparse coding conference, June 2010

General design algorithm for sparse frame expansions journal, January 2006

Deconvolutional networks conference, June 2010

Sparse and shift-Invariant representations of music journal, January 2006

Adaptive deconvolutional networks for mid and high level feature learning conference, November 2011

Multi-Scale Dictionary Learning Using Wavelets journal, September 2011

Dictionary Learning with Large Step Gradient Descent for Sparse Representations
book, January 2012

Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities
journal, August 2000

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
book, January 2011

A Sherman–Morrison approach to the solution of linear systems
journal, May 2006

From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
journal, February 2009

Task-Driven Dictionary Learning
journal, April 2012

An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
journal, March 2011

Atomic Decomposition by Basis Pursuit
journal, January 1998

A fast proximal method for convolutional sparse coding
conference, August 2013

Explicit Shift-Invariant Dictionary Learning
journal, January 2014

Updating the Inverse of a Matrix
journal, June 1989

Method of optimal directions for frame design
conference, January 1999

Fast Image Recovery Using Variable Splitting and Constrained Optimization
journal, September 2010

Learning mid-level features for recognition
conference, June 2010

On Shift-Invariant Sparse Coding
book, January 2004

Image compression using learned dictionaries by RLS-DLA and compared with K-SVD
conference, May 2011

Fast Convolutional Sparse Coding
conference, June 2013

Non-local sparse models for image restoration
conference, September 2009

Unsupervised learning of sparse and shift-invariant decompositions of polyphonic music
conference, January 2004

Supervised translation-invariant sparse coding
conference, June 2010

General design algorithm for sparse frame expansions
journal, January 2006

Deconvolutional networks
conference, June 2010

Sparse and shift-Invariant representations of music
journal, January 2006

Adaptive deconvolutional networks for mid and high level feature learning
conference, November 2011

Multi-Scale Dictionary Learning Using Wavelets
journal, September 2011