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Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches
ISR Systems Division, Space Dynamics Laboratory, 1695 North Research Park Way, North Logan,UT 84341, USA
Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84341, USA
Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84341,USA
* Author to whom correspondence should be addressed.
Received: 20 February 2013; in revised form: 12 April 2013 / Accepted: 12 April 2013 / Published: 19 April 2013
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
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MDPI and ACS Style
Stites, M.; Gunther, J.; Moon, T.; Williams, G. Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches. Remote Sens. 2013, 5, 1974-1997.
Stites M, Gunther J, Moon T, Williams G. Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches. Remote Sensing. 2013; 5(4):1974-1997.
Stites, Matthew; Gunther, Jacob; Moon, Todd; Williams, Gustavious. 2013. "Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches." Remote Sens. 5, no. 4: 1974-1997.