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Remote Sens. 2016, 8(3), 250; doi:10.3390/rs8030250

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
The Second Surveying and Mapping of Zhejiang Province, Hangzhou 310012, China
4
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)
View Full-Text   |   Download PDF [7202 KB, uploaded 16 March 2016]   |  

Abstract

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
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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).

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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.

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