The effect of denoising on superresolution of hyperspectral imaging

Armin Eskandari; Azam Karami

doi:10.1117/12.2278503

4 October 2017 The effect of denoising on superresolution of hyperspectral imaging

Armin Eskandari, Azam Karami

Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 1042708 (2017) https://doi.org/10.1117/12.2278503
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

Hyperspectral Images (HSI) are usually affected by different type of noises such as Gaussian and non-Gaussian. The existing noise can directly affect the classification, unmixing and superresolution analyses. In this paper, the effect of denoising on superresolution of HSI is investigated. First a denoising method based on shearlet transform is applied to the low-resolution HSI in order to reduce the effect of noise, then the superresolution method based on Bayesian sparse representation is used. The proposed method is applied to real HSI dataset. The obtained results of the proposed method in comparison with some of the state-of-the-art superresolution methods show that the proposed method significantly increases the spatial resolution and decreases the noise effects efficiently.

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Citation Download Citation

Armin Eskandari and Azam Karami "The effect of denoising on superresolution of hyperspectral imaging", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 1042708 (4 October 2017); https://doi.org/10.1117/12.2278503

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