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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)
Concentrations of microbiological contaminants in streams increase during rainfall-induced higher flow 'event' periods as compared to 'baseflow' conditions. If the stream feeds a drinking water reservoir, such periods of heightened pathogen loads may pose a challenge to the water treatment plant and subsequently a health concern to water consumers(More)
—This paper presents a semi-automated supervised level set hyperspectral image segmentation algorithm. The proposed method uses near-optimal speed functions (which control the level set segmentation) that are composed of a spectral similarity term and a stopping term. The spectral similarity term is used to compare pixels to class training signatures and is(More)
In a multi-source environment, each source has its own credibility. If there is no external knowledge about credibility then we can use the information provided by the sources to assess their credibility. In this paper, we propose a way to measure conflict in a multi-source environment as a normal measure. We examine our algorithm using three simulated(More)