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15 September 2014 Using kernel-based and single-scattering albedo approaches for generalized spectral mixture analysis of hyperspectral imagery
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Abstract
Spectral mixing can occur in a number of different ways, which may be linear or non-linear. Perhaps the pixel size of a sensor is just too large so many pixels contain patches of different materials within them resulting in linear mixing of the materials. However, there are more complex situations, such as scattering that occurs in mixtures of vegetation and soil, or intimate mixing of granular materials like soils. Such multiple scattering and microscopic mixtures within pixels have varying degrees of non-linearity. Often enough, scenes may contain cases of both linear and non-linear mixing on a pixel-by-pixel basis. This study compares two approaches for use as generalized methods for un-mixing pixels in a scene that may be linear or non-linear. The first is a kernel-based fully-constrained method for spectral unmixing, which uses a kernel that seeks to capture the linear behavior of albedo in non-linear mixtures of materials. The second method directly converts reflectance to single-scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. Multiple scenes of hyperspectral imagery calibrated to reflectance are used to validate the methods. We test the approaches using a HyMAP scene collected over the Waimanalo Bay region in Oahu, Hawaii, as well as an AVIRIS scene collected over the oil spill region in the Gulf of Mexico during the Deepwater Horizon oil incident.
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Robert S. Rand and Ronald G. Resmini "Using kernel-based and single-scattering albedo approaches for generalized spectral mixture analysis of hyperspectral imagery", Proc. SPIE 9222, Imaging Spectrometry XIX, 92220J (15 September 2014); https://doi.org/10.1117/12.2063278
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