Synthetic Aperture Radar Image Processing Based On Independent Component Analysis

SAR (Synthetic Aperture Radar) images get the rapid development and wide application because they can be obtained any-time, any-weather and with high resolution. Unfortunately, SAR images are always polluted by the multiplicative noise. For this reason, the traditional methods of image processing do not work well.In this paper, a series of new methods are proposed and applied to SAR compression, speckle reduction and enhancement. The main innovative points are as follows:At first, a new method based on multiscale autoregressive moving average (MARMA) models is presented to compress SAR image. The method uses the multiscale representation as the cornerstone of the modeling process, and constructs the MARMA models of image. Thus we predict the initialized image data using these multiscale models, and the compression is subsequently achieved through coding the residual image. Unlike published methods, supervising segmentation for SAR image is not used in our compression processes. So the prior knowledge of segmentation is not required. Experimental results have proven that the proposed method achieves high compression radios with impressive image quality.Secondly, we present a new algorithm for SAR image compression based on projection pursuit learning networks. At first, we segment an SAR image into regions of different sizes based on mean value in each region and then constructing a distinct code for each block by using the projection pursuit neural networks. The process is stopped when the desired error threshold is achieved. The experimental results show that excellent performance can be achieved for typical SAR images with no significant distortion introduced by image compression.Thirdly, the polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, we proposed a new speckle reduction approach using ICA based on statistical formulation of PSAR image. In addition, we apply three ICA algorithms on real PSAR images and compare their performances. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing the results obtained earlier.Fourthly, we propose a robust ICA network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations of separation of noisy image confirm robustness and the excellent performance of the resulting algorithms. In addition, its application in SAR is discussed. The results show the potential usage in SAR image processing problems.Fifthly, the basic model and methods of subband Independent Component Analysis (SICA) system are introduced in this paper. In our model, the assumption of the standard ICA model that the source signals are mutually independent is relaxed. We presume that the source signals are the sum of some independent and/or dependent subcomponents. This blind source separation (BSS) problem is solves by using the subband decomposition as preprocessing of ICA. The method proposed in the paper has been tested for unsupervised separation and enhancement in SAR images. The results indicate that the method is promising for the analysis problem of SAR images. In addition, we use SICA to speckle reduction of PSAR images. The results indicate that the method is promising for the speckle reduction problem of PSAR images.