Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMFReport as inadecuate




Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF - Download this document for free, or read online. Document in PDF available to download.

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: AndrĂ¡s Jung, Lenio Soares Galvao and Prasad S. Thenkabail

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





Author: Rong Liu 1, Bo Du 2,* and Liangpei Zhang 1

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents