Denoising of Hyperspectral Remote Sensing Image using Multiple Linear Regression and Wavelet Shrinkage
Dong Xu, Lei Sun, Jianshu Luo
Available Online March 2013.
- 10.2991/icibet.2013.137How to use a DOI?
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.
- © 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/).
Cite this article
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 -