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Optical Engineering

Hyperspectral image fusion by the similarity measure-based variational method
Author(s): Zhenwei Shi; Zhenyu An; Zhiguo Jiang
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Paper Abstract

Hyperspectral remote sensing is widely used in many fields suchas agriculture, military detection, mineral exploration, and so on. Hyperspectral data has very high spectral resolution, but much lower spatial resolution than the data obtained by other types of sensors. The low spatial resolution restrains its wide applications. On the contrary, we easily obtain images with high spatial resolution but insufficient spectral resolution (like panchromatic images). Naturally, people expect to obtain images that have high spatial and spectral resolution at the same time by the hyperspectral image fusion. In this paper, a similarity measure-based variational method is proposed to achieve the fusion process. The main idea is to transform the image fusion problem to an optimization problem based on the variational model. We first establish a fusion model that constrains the spatial and spectral information of the original data at the same time, then use the split bregman iteration to obtain the final fused data. Also, we analyze the convergence of the method. The experiments on the synthetic and real data show that the fusion method preserves the information of the original images efficiently, especially on the spectral information.

Paper Details

Date Published: 1 July 2011
PDF: 12 pages
Opt. Eng. 50(7) 077006 doi: 10.1117/1.3600767
Published in: Optical Engineering Volume 50, Issue 7
Show Author Affiliations
Zhenwei Shi, BeiHang Univ. (China)
Zhenyu An, BeiHang Univ. (China)
Zhiguo Jiang, BeiHang Univ. (China)

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