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

Patent ·
OSTI ID:1435634
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.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC52-06NA25396
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
Patent Number(s):
9,946,931
Application Number:
15/133,387
OSTI ID:
1435634
Country of Publication:
United States
Language:
English

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