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A spatial regularized local manifold learning (SR_LML) approach is investigated for dimensionality reduction (DR) of hyperspectral images, where the spatial regularizer constrains the spatial neighbors with high spectral similarity to have similar embedding coordinates. SR_LML requires the processed data to contain good spatial relations, but the requirement may not be satisfied. We solve the problem...
Anomaly detection in hyperspectral images is investigated using local tangent space alignment (LTSA) for dimensionality reduction (DR) in conjunction with a minimum distance detector. The LTSA is implemented for large images by constructing a manifold with training data and employing the out-of-sample extension for testing data. The training data that should represent all the background types are...
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