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Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery
Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery
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机译：多光谱和高光谱卫星图像中自然和人为特征的变化检测和变化监控
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摘要
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.
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