John E. Ball

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—We present a semi-automated supervised hyper-spectral image segmentation algorithm based on the level set methodology. In the proposed procedure, seed pixels are automatically selected by their similarity to the training signatures, and speed functions that control the level set propagation are created based on pixel similarity to the seed signature and(More)
—We present a supervised hyperspectral classification procedure consisting of an initial distance-based segmentation method that uses best band analysis (BBA), followed by a level set enhancement that forces localized region homogeneity. The proposed method is tested on two hyperspectral images of an urban and rural nature. The proposed method is compared(More)
—We present a novel approach to two-class linear hyperspectral pixel unmixing. The mixed pixel signatures and training signatures are preprocessed by low pass filtering, and then a best bands analysis determines which bands to use in the final unmixing stage. Abundance estimate statistical properties and partial residues are utilized in adapting the(More)