[Show abstract][Hide abstract] ABSTRACT: Noise estimation of hyperspectral remote sensing image is In this paper, not only the spectral correlation removing is considered, but the spatial correlation removing by wavelet transform is considered as well. Therefore, a new method based on multiple linear regression (MLR) and wavelet transform is proposed to estimate the noise of hyperspectral remote sensing image. Numerical simulation of AVIRIS data is carried out and the real data Hyperion is also used to validate the proposed algorithm. Experimental results show that the method is more adaptive and accurate than the general MLR and the other classified methods.
Boletim de Ciências Geodésicas. 12/2013; 19(4):639-652.
[Show abstract][Hide abstract] ABSTRACT: A new denoising algorithm is proposed according to the characteristics of hyperspectral remote sensing image (HRSI) in the curvelet domain. Firstly, each band of HRSI is transformed into the curvelet domain, and the sets of subband images are obtained from different wavelength of HRSI. And then the detail subband images in the same scale and same direction from different wavelengths of HRSI are stacked to obtain new 3-D datacubes of the curvelet domain. Again, the characteristics analysis of these 3-D datacubes is performed. The analysis result shows that each new 3-D datacube has the strong spectral correlation. At last, due to the strong spectral correlation of new 3-D datacubes, the multiple linear regression is introduced to deal with these new 3-D datacubes in the curvelet domain. The simulated and the real data experiments are performed. The simulated data experimental results show that the proposed algorithm is superior to the compared algorithms in the references in terms of SNR. Furthermore, MSE and MSSIM in each band are utilized to show that the proposed algorithm is superior. The real data experimental results show that the proposed algorithm effectively removes the common spotty noise and the strip noise and simultaneously maintains more fine features during the denoising process.
Mathematical Problems in Engineering 07/2013; 2013. · 1.38 Impact Factor
[Show abstract][Hide abstract] 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.
Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013); 03/2013