An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction

Xuejian Sun; Lifu Zhang; Yi Cen; Mingyue Zhang

doi:10.1117/12.2225912

19 May 2016 An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction

Xuejian Sun, Lifu Zhang, Yi Cen, Mingyue Zhang

Author Affiliations +

Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 98740P (2016) https://doi.org/10.1117/12.2225912
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Xuejian Sun, Lifu Zhang, Yi Cen, and Mingyue Zhang "An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740P (19 May 2016); https://doi.org/10.1117/12.2225912

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