Next Article in Journal
Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates
Next Article in Special Issue
Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation
Previous Article in Journal
Using MODIS Data to Predict Regional Corn Yields
Previous Article in Special Issue
Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(1), 15; doi:10.3390/rs9010015

Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery

College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China
Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politecnica, University of Exremadura, Cáceres 10003, Spain
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Jonathan Cheung-Wai Chan, Yongqiang Zhao, Naoto Yokoya and Prasad S. Thenkabail
Received: 5 September 2016 / Revised: 17 December 2016 / Accepted: 21 December 2016 / Published: 29 December 2016
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
View Full-Text   |   Download PDF [3085 KB, uploaded 29 December 2016]   |  


Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution) remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV) regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods. View Full-Text
Keywords: sub-pixel mapping; super-resolution mapping; spectral unmixing; endmember variability; hyperspectral imaging; sparse regression sub-pixel mapping; super-resolution mapping; spectral unmixing; endmember variability; hyperspectral imaging; sparse regression

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).

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

Xu, X.; Tong, X.; Plaza, A.; Zhong, Y.; Xie, H.; Zhang, L. Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery. Remote Sens. 2017, 9, 15.

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



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