Next Article in Journal
Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products
Previous Article in Journal
An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(8), 841; doi:10.3390/rs9080841

A Probabilistic Weighted Archetypal Analysis Method with Earth Mover’s Distance for Endmember Extraction from Hyperspectral Imagery

1
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
2
State Key Lab of Information Engineering on Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
4
Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
*
Authors to whom correspondence should be addressed.
Received: 1 April 2017 / Revised: 6 August 2017 / Accepted: 9 August 2017 / Published: 14 August 2017
View Full-Text   |   Download PDF [3545 KB, uploaded 14 August 2017]   |  

Abstract

A Probabilistic Weighted Archetypal Analysis method with Earth Mover’s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers manifold structures of spectral signatures in the HSI data and could better quantify the dissimilarity features among pairwise pixels. Second, the PWAA-EMD adopts the Bayesian framework and formulates the improved AA into a probabilistic inference problem by maximizing a joint posterior density. Third, the optimization problem is solved by the iterative multiplicative update scheme, with a careful initialization from the two-stage algorithm and the proper endmembers are finally obtained. The synthetic and real Cuprite Hyperspectral datasets are utilized to verify the performance of PWAA-EMD and five popular methods are implemented to make comparisons. The results show that PWAA-EMD surpasses all the five methods in the average results of spectral angle distance (SAD) and root-mean-square-error (RMSE). Especially, the PWAA-EMD obtains more accurate estimation than AA in almost all the classes of endmembers including two similar ones. Therefore, the PWAA-EMD could be an alternative choice for endmember extraction on the hyperspectral data. View Full-Text
Keywords: probabilistic weighted archetypal analysis; earth mover’s distance; endmember extraction; hyperspectral imagery probabilistic weighted archetypal analysis; earth mover’s distance; endmember extraction; hyperspectral imagery
Figures

Figure 1

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. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Sun, W.; Zhang, D.; Xu, Y.; Tian, L.; Yang, G.; Li, W. A Probabilistic Weighted Archetypal Analysis Method with Earth Mover’s Distance for Endmember Extraction from Hyperspectral Imagery. Remote Sens. 2017, 9, 841.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

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