Abstract
This study considers a physics-based and a kernel-based approach for characterizing pixels in a scene that may be linear (areal mixed) or nonlinear (intimately mixed). The physics-based method is based on earlier studies that indicate nonlinear mixtures in reflectance space are approximately linear in albedo space. The approach 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. The kernel-based method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in nonlinear mixtures of materials. The behavior of the kernel method is dependent on the value of a parameter, gamma. Validation of the two approaches is performed using laboratory data.
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Photographs in Figures 1 and 2 taken by the co-author Dr. David W. Allen of NIST and owned by the U.S. Government.
http://www.resonon.com/imagers_pika_iii.html (last Accessed on 3 Dec 2013)
We have also used an Edmund Optics Gold Series 1.0X telecentric lens that gives ~8 μm/pixel. However, data at such a high spatial resolution were not required for the analyses reported upon here
Note: References are made to certain commercially available products in this paper to adequately specify the experimental procedures involved. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that these products are the best for the purpose specified
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Acknowledgements
The MITRE Innovation Program (MIP) is gratefully acknowledged for funding the HSI Microscopy aspect of the project in which the study presented here was conducted.
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Rand, R.S., Resmini, R.G., Allen, D.W. (2017). Approaches for Characterizing Nonlinear Mixtures in Hyperspectral Imagery. In: Balan, R., Benedetto, J., Czaja, W., Dellatorre, M., Okoudjou, K. (eds) Excursions in Harmonic Analysis, Volume 5. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-54711-4_5
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