Characteristic Analysis of SAR Noise Based on Wavelet Transform

For finding out behaviour characteristics of SAR image in Wavelet domain and utilizing Wavelet analysis technique to process SAR images well, this paper has carried on the thorough analysis for the characteristics of SAR image Wavelet coefficients from two aspects of energy and statistics distribute. Firstly, SAR image is transformed from the spatial domain to the Wavelet domain by Wavelet transformation algorithm, and the energy distribution characteristics of Wavelet coefficients in low frequency son-band are researched, then the energy distribution rules of Wavelet coefficients in high frequency (HF) son-bands are discussed with emphasis. From the analysis results obtained in this study, these conclusions can be made that SAR image Wavelet coefficients have the characteristic of energy accumulation in low frequency part, and has some characteristics in high frequency part, i.e., energy accumulation of high frequency remarkable coefficients, standard normal distribution of coefficients, the special correlation between energy tendency on each scale and noise size. These characteristics provide more spaces for noise analysis of SAR image, edge information examination and data compression based on Wavelet transform algorithm.

[1]  Lin Zhe.  EM ALGORITHM FOR ESTIMATING THE NOISE DEVIATION OF THE IMAGE IN THE WAVELET DOMAIN , 2001 .

[2]  Zhou Chunxia.  Feasibility of InSAR Application to Antarctic Mapping , 2004 .

[3]  Zhao Jian,et al.  SAR image denoising based on wavelet-fractal analysis , 2007 .

[4]  En-Bing Lin,et al.  Image compression and denoising via nonseparable wavelet approximation , 2003 .

[5]  Shi Shiping.  DEM generation using ERS-1/2 interferometric SAR data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[6]  Qingwei Gao,et al.  Despeckling SAR images using stationary wavelet transform combining with directional filter banks , 2008, Appl. Math. Comput..

[7]  Liu Guo,et al.  Experimental Investigation on DEM Generation through InSAR , 2001 .

[8]  Fuk K. Li,et al.  Synthetic aperture radar interferometry , 2000, Proceedings of the IEEE.

[9]  Obtaining digital elevation data in different terrain and physiognomy regions with spaceborne InSAR and its application analysis , 2002 .

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Shuyuan Yang,et al.  Low bit rate SAR image coding based on adaptive multiscale Bandelets and cooperative decision , 2009, Signal Process..

[12]  FIELD COLLECTED PLANT SPECTRUM DENOISING BY LOGARITHM TRANSFORM AND WAVELET TRANSFORM: FIELD COLLECTED PLANT SPECTRUM DENOISING BY LOGARITHM TRANSFORM AND WAVELET TRANSFORM , 2009 .

[13]  K. K. Gupta,et al.  Despeckle and geographical feature extraction in SAR images by wavelet transform , 2007 .

[14]  Zhang Qi.  IDETIFYING OF NOISE TYPES AND ESTIMATING OF NOISE LEVEL FOR A NOISY IMAGE IN THE WAVELET DOMAIN , 2004 .