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Superresolution of hyperspectral images using backpropagation neural networks

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3 Author(s)
Mianji, F.A. ; Sch. of Electron. & Inf. Tech., Harbin Inst. of Technol., Harbin, China ; Ye Zhang ; Babakhani, A.

Hyperspectral technology has introduced a new perspective in remote sensing applications but suffers from low spatial resolution. A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end a linear mixture model and a fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. To validate the performance of the method, experiments are carried out on real images. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is low in terms of computational cost.

Published in:

Nonlinear Dynamics and Synchronization, 2009. INDS '09. 2nd International Workshop on

Date of Conference:

20-21 July 2009