Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery
First Claim
1. A computer-implemented method, comprising:
- learning representative land features, by a computing system, from multi-band patches or pixels of image data to form a learned feature dictionary;
applying representative features from the learned feature dictionary, by the computing system, to image patches or pixels from an image in a sparse fashion to form a sparse representation;
clustering the sparse representation of the features in the image, by the computing system, into land cover categories; and
performing land cover classification and change detection in the sparse domain, by the computing system, after the image is clustered and outputting results.
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Accused Products
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.
27 Citations
12 Claims
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1. A computer-implemented method, comprising:
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learning representative land features, by a computing system, from multi-band patches or pixels of image data to form a learned feature dictionary; applying representative features from the learned feature dictionary, by the computing system, to image patches or pixels from an image in a sparse fashion to form a sparse representation; clustering the sparse representation of the features in the image, by the computing system, into land cover categories; and performing land cover classification and change detection in the sparse domain, by the computing system, after the image is clustered and outputting results. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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Specification