Denoising of Hyperspectral Remote Sensing Image using Multiple Linear Regression and Wavelet Shrinkage

DOI

10.2991/icibet.2013.137How to use a DOI?

Abstract

Hyperspectral remote sensing image is easily contaminated by noise, which will affect the application of hyperspectral image, such as target detection, classification and segmentation, etc. Therefore, a denoising method of hyperspectral remote sensing image based on multiple linear regression (MLR) and wavelet shrinkage (WS) is proposed. Firstly, the residual image and the predicted image are obtained via MLR. Secondly, WS is performed on the residual image to remove the noise in the spatial domain. Lastly, a final denoised image is obtained by the predicted image and the corrected residual image. The experimental results show that the proposed method can improve signal-to-noise ratio (SNR) of the hyperspectral image efficiently.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dong Xu
AU  - Lei Sun
AU  - Jianshu Luo
PY  - 2013/03
DA  - 2013/03
TI  - Denoising of Hyperspectral Remote Sensing Image using Multiple Linear Regression and Wavelet Shrinkage
BT  - Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
PB  - Atlantis Press
SP  - 639
EP  - 642
SN  - 1951-6851
UR  - https://doi.org/10.2991/icibet.2013.137
DO  - 10.2991/icibet.2013.137
ID  - Xu2013/03
ER  -