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• W2131456955 abstract "This paper addresses a conceptual approach to hyperspectral image assessment that uses physics-based models to constrain multiparameter inversion algorithms aimed at quantitative measurement of material properties. Sensing approaches include use of a single hyperspectral image with many spectral samples, a sequence of images acquired over time, and widely different types of measurements from a range of sensor types. We propose a conceptual approach for merging these data into a common framework to allow simultaneous exploitation of these multiparameter data sets. Physics-based models predict or constrain the range of observable parameters associated with a target or material condition, then model matching or optimization methods invert a measurement set to a target type or condition. 2 examples are presented using hyperspectral data sets. The first involves characterizing atmospheric constituents using the MODTRAN Code to constrain the solutions. The second uses a radiation propagation model to drive an inversion of hyperspectral data to multiple water quality parameters. Finally, we discuss how more involved 3D physics-based synthetic image models may hold a key to image exploitation algorithms." @default.
• W2131456955 created "2016-06-24" @default.
• W2131456955 creator A5024881585 @default.
• W2131456955 date "2002-11-11" @default.
• W2131456955 modified "2023-09-25" @default.
• W2131456955 title "Combining image derived spectra and physics based models for hyperspectral image exploitation" @default.
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• W2131456955 doi "https://doi.org/10.1109/aiprw.2000.953598" @default.
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