Nonlinear Unmixing for Imaging Spectroscopy
|Paul Gader, University of Florida, email@example.com
|Rob Heylen, University of Antwerp, Belgium, firstname.lastname@example.org
This tutorial will focus on physical models and computational algorithms for nonlinearly unmixing pixels acquired by imaging spectrometers. Examples will be given on Earth Science and Planetary data. Some examples will be demonstrated with MATLAB scripts.
The tutorial will cover the following models / algorithms:
- Linear / Convex (Review)
- Linear with non-linear perturbations
- Piecewise Convex
- Microscopic using Hapke and relate approximations
- Ray Tracing and Radiosity
- Kernel Methods
- Neural Networks
- Manifold Learning
Nonlinear mixing occurs in imaging spectroscopy in many ways, most notably in microscopic or intimate mixing of small particles and multiple scattering in urban and forested environments. While linear mixing is mainly due to finite resolution of optical systems and the need to create relatively large pixels, non-linear mixing is based on the direct interaction of light with matter in the field of view. Therefore, parameters of approximate physical models of the interaction are often estimated as a part of the unmixing process. Using a linear mixing model when a nonlinear mixing model is more accurate can result in very large errors in estimating characteristics of sub-pixel materials. Therefore, better models and algorithms are needed.
The instructors have co-authored a review paper:R. Heylen, M. Parente, P. Gader, "A Review of Nonlinear Hyperspectral Unmixing Methods," in IEEE J. Selected Topics Applied Earth Observations and Remote Sensing, v. 7, n. 6, pp. 1844-1868, June 2014. doi: 10.1109/JSTARS.2014.2320576 that contains material on many methods(excluding the most recent) that will be presented. A laptop computer with MATLAB would be useful.
Keywords: Hapke models, Canopy models, Mineral and Planetary spectroscopy, hyperspectral mixing and unmixing
Number of Participants: max. 20
Tutorial Fee: 100 Euro
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