Next Article in Journal
Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation
Previous Article in Journal
Comparison of Geophysical Model Functions for SAR Wind Speed Retrieval in Japanese Coastal Waters
Article Menu

Export Article

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

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 / Revised: 12 April 2013 / Accepted: 12 April 2013 / Published: 19 April 2013
View Full-Text   |   Download PDF [350 KB, uploaded 19 June 2014]   |  


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. View Full-Text
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 (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top