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Proceedings Paper

An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction
Author(s): Xuejian Sun; Lifu Zhang; Yi Cen; Mingyue Zhang
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Paper Abstract

In order to get high spatial resolution hyperspectral data, many studies have examined methods to combine spectral information contained in hyperspectral image with spatial information contained in multispectral/panchromatic image. This paper developed a new hyperspectral image fusion method base on the non-negative matrix factorization (NMF) theory. Data sets obtained by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to evaluate the performance of the method. Experimental results show that the proposed algorithm can provide a good way to solve the problem of high spatial resolution hyperspectral data shortage.

Paper Details

Date Published: 19 May 2016
PDF: 7 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740P (19 May 2016); doi: 10.1117/12.2225912
Show Author Affiliations
Xuejian Sun, Institute of Remote Sensing and Digital Earth (China)
Lifu Zhang, Institute of Remote Sensing and Digital Earth (China)
Yi Cen, Institute of Remote Sensing and Digital Earth (China)
Mingyue Zhang, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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