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Remote Sens. 2016, 8(6), 464; doi:10.3390/rs8060464

Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF

1
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Computer, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: András Jung, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 11 March 2016 / Revised: 19 May 2016 / Accepted: 24 May 2016 / Published: 31 May 2016
View Full-Text   |   Download PDF [11386 KB, uploaded 31 May 2016]   |  

Abstract

Hyperspectral unmixing aims to obtain the hidden constituent materials and the corresponding fractional abundances from mixed pixels, and is an important technique for hyperspectral image (HSI) analysis. In this paper, two characteristics of the abundance variables, namely, the local spatial structural feature and the statistical distribution, are incorporated into nonnegative matrix factorization (NMF) to alleviate the non-convex problem of NMF and enhance the hyperspectral unmixing accuracy. An adaptive local neighborhood weight constraint is proposed for the abundance matrix by taking advantage of the spatial-spectral information of the HSI. The spectral information is utilized to calculate the similarities between pixels, which are taken as the measurement of the smoothness levels. Furthermore, because abrupt changes may appear in transition areas or outliers may exist in spatially neighboring regions, any inappropriate smoothness constraint on these pixels is removed, which can better express the local smoothness characteristic of the abundance variables. In addition, a separation constraint is used to prevent the result from over-smoothing, preserving the inner diversity of the same kind of material. Extensive experiments were carried out on both simulated and real HSIs, confirming the effectiveness of the proposed approach. View Full-Text
Keywords: hyperspectral unmixing; mixed pixels; abundance smoothness; selected local neighborhood; nonnegative matrix factorization (NMF) hyperspectral unmixing; mixed pixels; abundance smoothness; selected local neighborhood; nonnegative matrix factorization (NMF)
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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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MDPI and ACS Style

Liu, R.; Du, B.; Zhang, L. Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF. Remote Sens. 2016, 8, 464.

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