Title: Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

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. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images 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; Wohlberg, Brendt

Issue Date:

2018-01-02

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1415441

Patent Number(s):

9858502

Application Number:

15/134,437

Assignee:

Los Alamos National Security, LLC (Los Alamos, NM)

Patent Classifications (CPCs):

G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING

H - ELECTRICITY H03 - BASIC ELECTRONIC CIRCUITRY H03M - CODING

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G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING

G06F17/14 - Fourier, Walsh or analogous domain transformations {, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms

H - ELECTRICITY H03 - BASIC ELECTRONIC CIRCUITRY H03M - CODING

H03M7/3059 - {Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression}
H03M7/3084 - {using adaptive string matching, e.g. the Lempel-Ziv method}

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DOE Contract Number:  

AC52-06NA25396

Resource Type:

Patent

Resource Relation:

Patent File Date: 2016 Apr 21

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; 47 OTHER INSTRUMENTATION