Research On Sar Image De-Noising Based On Multi-Scaled Analysis And Independent Component Analysis

Synthetic Aperture Radar (SAR) sensors can produce range imagery of high spatial resolution under all-weather conditions. It can also effectively identify, even penetrate the disguise easily. Therefore, it has been obtained extensive and intensive applications on both military and civil aspects. However, because the SAR image data are formed by coherent interaction of the transmitted microwave with the targets, it suffers from the effects of speckle noise which arises from coherent summation of the signals scattered from scatterers. This kind of noise makes image understanding a very hard job and severely affects its further application. To remove the noise is necessary and has also been obtained great attention from researchers all over the world.Traditional methods are mainly based on spatial filtering or to design linear filters according to specific criterion. Different from which, this paper presents a novel approach using multi-scaled analysis and independent component analysis on SAR image speckle reduction, focusing on both the multi-resolution and great internal information of SAR image.The main research work of this paper concludes:Introduction section presents the current research situation on speckle reduction and the key work of this paper. The second section gives the speckle model of SAR images, typical disspeckling algorithms and assessment indicators on recovery image. In the third section, multi-scaled analysis is presented, in which, multifractal and wavelet are respectively used for SAR image de-noising. Regarding to multifractal theory, some basic knowledge is firstly given, then pointwise H?lder exponent computing and multi-spectral analysis, noise reduction are orderly explained. Regarding to wavelet, the typical thresholding shrinkage algorithm is presented. Its key steps including threshold determination and thresholding function design are also presented. The next section is about speckle reduction based on independent component analysis (ICA), comprised of ICA overview, model solutions and classical ICA sparse coding shrinkage. In the fifth section, presented is de-noising methods taking advantage on multi-scaled analysis and ICA. Conclusions and research prospects are given in the final section.The creative aspects proposed by this paper conclude:Firstly, using binary morphology and average filtering to optimize experimental results.Secondly, proposing adaptive space separation based on ICA. From the view of signal separation, we separate the original image space into noise space and non-noise space by setting proper threshold. Beyond that,"threshold-weighted information entropy function"is interpolated to obtain adaptive threshold.Thirdly, proposing WF (Wavelet--Fractal) algorithm from experimental view, taking advantage of incorporation of both fractal and wavelet.Fourthly, proposing WCA (Wavelet--Independent Component Analysis approach) algorithm taking use of frequency domain technique and high-order statistic information.Fifthly, proposing FCA (Fractal--Independent component analysis) algorithm using basis image enhancement and separation, taking advantage of mining basis image information.Sixthly, proposing H-ICA (H?lder--Independent component analysis) algorithm based on H?lder image coding shrinkage, taking advantage of the good describing ability of H?lder exponent.