Research On The Key Technologies For Target Classification In SAR Imagery

Aiming at developing automatic and semi-automatic systems of SAR image classification, considering imaging reconnaissance applications and the demand of building and developing radar imaging satellites, spacecraft and ground application systems, with the background of SAR image target interpretation applications and the support of abundant high-resolution SAR data, this thesis comprehensively studies the techniques of SAR image despeckling, SAR image segmentation, feature extraction from SAR chips for target classification. The research works of this thesis are as follows:SAR image despeckling is developed in chapter 2. Despeckling is a basic and important subject of SAR image processing, which focuses on suppressing the speckle while preserving the structural features such as point, line and edge. First, a structure-preserving MRF model, the SPMRF model, is proposed and the parameter estimation method is given. This model can adaptively adjust the weights according to the local features in image. It can be used to describe both homogeneous regions and structural features. It provides accurate prior knowledge and leads to good despeckling performance. Then, based on the idea of adaptive window, a method of adaptively adjusting the MRF neighborhood is presented, which solves the problem that simple MRF model can't preserve the structural feature. The neighborhood is adjusted via determining the homogeneity of local region. Larger neighborhood is adopted in homogeneous regions to suppress speckle. Smaller neighborhood is adopted in regions with structural features to preserve the structural feature by selecting the pixels which are most likely to have the same backscattering properties as the center pixel. In this way, the AN-MMRF despeckling method preserves the structural feature while suppressing speckle. Finally, combining the HMT-based and HMRF-based hidden state estimation methods, a HMT-HMRF-model-based method for SAR image wavelet coefficients is proposed, which utilizes the correlation within and between scales of wavelet coefficients and improves the accuracy of estimation. The corresponding wavelet despeckling method achieves good performance.SAR image segmentation is discussed in chapter 3. In order to extract target ROI chips from large-scene SAR images and separating the target regions, three segmentation methods are studied, namely, segmentation based on maximum-between-class-variance, segmentation based on fractal features and segmentation based on multi-resolution analysis. First, based on the existing maximum-between-class-variance segmentation, 2D histogram is analyzed on image with multiplicative noise and a new method of segmenting histogram with multiplicative noise is proposed. The rule for determining threshold is also provided. Based on the improved 2D histogram segmentation and new thresholding method, SAR image segmentation based on maximum-between-class–variance is presented. Second, considering the textural feature of SAR image, fractal theory is used to compute the fractal dimension and spacing feature to measure the fluctuating properties of local data. Based on the two features and quadratic distance function, SAR image segmentation is implemented. Finally, in order to remove the influence of speckle on high-resolution SAR image segmentation, segmentation method based on multi-resolution analysis is proposed. In this method, multi-resolution processing is implemented on images, and then MAR model are built on multi-scale data. Multi-resolution log-likelihood ratio is computed to achieving SAR image segmentation.Target feature extraction for classification is presented in chapter 4. Established in constructing an automatic and semi-automatic SAR image target classification system, the target chip extraction for SAR target classification is deeply studied. First, the techniques to estimate SAR target orientation angle are reviewed, and a method jointing dominant boundaries and minimum outer rectangles is proposed to estimate the SAR target orientation angle. The method, considering the advantages and disadvantages of that only uses dominant boundaries or outer rectangles, can significantly improve the estimation accuracy of target orientation angle. Then, established in constructing a semi-automatic SAR target classification system, the extraction of image geometric features is studied, and several intuitive and effective target geometric features are extracted. Finally, a fast SAR target recognition method is proposed. The method uses Principle Component Analysis (PCA) based on learning rules to extract target features, and Multiple Levels Perception Neural Network (MLPNN) to classify the target. The automatic and fast target classification is thus finished.