Next Article in Journal
A Two-Source Model for Estimating Evaporative Fraction (TMEF) Coupling Priestley-Taylor Formula and Two-Stage Trapezoid
Next Article in Special Issue
Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery
Previous Article in Journal
Correction of Incidence Angle and Distance Effects on TLS Intensity Data Based on Reference Targets
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 250;

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
The Second Surveying and Mapping of Zhejiang Province, Hangzhou 310012, China
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Author to whom correspondence should be addressed.
Academic Editors: Jonathan Cheung-Wai Chan, Yongqiang Zhao, Naoto Yokoya, Magaly Koch and Prasad S. Thenkabail
Received: 12 December 2015 / Revised: 3 March 2016 / Accepted: 11 March 2016 / Published: 16 March 2016
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Full-Text   |   PDF [7202 KB, uploaded 16 March 2016]   |  


Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations. View Full-Text
Keywords: subpixel mapping; nonlocal total variation; hyperspectral remote sensing imagery; spatial regularization subpixel mapping; nonlocal total variation; hyperspectral remote sensing imagery; spatial regularization

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

Feng, R.; Zhong, Y.; Wu, Y.; He, D.; Xu, X.; Zhang, L. Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery. Remote Sens. 2016, 8, 250.

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