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12 August 2004 Generation of synthetic HSI data using linear mixing model with Gaussian endmembers
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Well-chosen background models are critical to accurately predict the performance of hyperspectral detection and classification algorithms and to evaluate the effects on system performance of variation in environmental or sensor parameters. Such models also have implications for the derivation of optimal algorithms. First-principal physical models and statistical models have been developed for these purposes. However, in many circumstances these models may not accurately represent hyperspectral data that are complicated by intra-class variability and subpixel mixing of materials as well as atmospheric, illumination, temperature (in the emissive regime) and sensor effects. In this paper we propose a statistical representation of hyperspectral data defined by class parameters and an abundance probability distribution. Various representations of the probability distribution function of the abundance values are developed and compared with data to determine if the estimated abundance distributions and intra-class variation explain the observed heavy tails in the data. The consequences of the Gaussian endmembers of the normal compositional model violating the non-negativity constraint are also investigated.
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David Walter Jacques Stein and Dimitris G. Manolakis "Generation of synthetic HSI data using linear mixing model with Gaussian endmembers", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004);

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