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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 751716, 11 pages
Research Article

Analysis and Denoising of Hyperspectral Remote Sensing Image in the Curvelet Domain

1College of Science, National University of Defense Technology, Changsha, Hunan 410073, China
2Department of Radiation Oncology, The 89th Hospital of PLA, Weifang, Shandong 261045, China

Received 7 May 2013; Accepted 1 July 2013

Academic Editor: Yue Wu

Copyright © 2013 Dong Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.