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CLASSIFICATION OF MULTISPECTRAL OR HYPERSPECTRAL SATELLITE IMAGERY USING CLUSTERING OF SPARSE APPROXIMATIONS ON SPARSE REPRESENTATIONS IN LEARNED DICTIONARIES OBTAINED USING EFFICIENT CONVOLUTIONAL SPARSE CODING

United States Patent Application

20170213109
A1
View the Complete Application at the US Patent & Trademark Office
Los Alamos National Laboratory - Visit the Technology Transfer Division Website
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.
Moody, Daniela (Los Alamos, NM), Wohlberg, Brendt (Los Alamos, NM)
Los Alamos National Security, LLC (Los Alamos NM)
15/ 134,437
April 21, 2016
STATEMENT OF FEDERAL RIGHTS [0002] The United States government has rights in this invention pursuant to Contract No. DE-AC52-06NA25396 between the United States Department of Energy and Los Alamos National Security, LLC for the operation of Los Alamos National Laboratory.