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Remote Sens. 2013, 5(4), 1974-1997; doi:10.3390/rs5041974
Article

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

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Received: 20 February 2013 / Revised: 12 April 2013 / Accepted: 12 April 2013 / Published: 19 April 2013
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Abstract

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.
Keywords: independent component analysis (ICA); FastICA; hyperspectral unmixing; abundance quantification; DIRSIG independent component analysis (ICA); FastICA; hyperspectral unmixing; abundance quantification; DIRSIG
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Stites, M.; Gunther, J.; Moon, T.; Williams, G. Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches. Remote Sens. 2013, 5, 1974-1997.

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