Study On SAR Image Pressing And Ground Target Recognition Technology

Synthetic Aperture Radar (SAR) ground target recognition plays an important role in battlefield awareness. As one of major source of ground information, it can provide powerful support to war decision. This dissertation addresses issues of SAR ground target recognition technology. The work of this dissertation mainly focuses on two aspects: The first one is SAR image pre-processing, and the second one are SAR target characteristics extraction and SAR target recognition. The main content of this dissertation is summarized as follows.The first part of this dissertation presents a composite adaptive enhancing and denoising algorithm for SAR images. The SAR images are differentiated from speckle noise via scale space correlation. Because SAR image can be described by approximate rayleigh's distribution, complex diffusion coupled shock filter is used to enhance the signal differentiated from SAR image, and the anisotropic diffusion equation is used to denoise the speckle in SAR image. At last, the SAR image is reconstructed by stationary wavelet transform. Compared with traditional speckle removal algorithms, this new algorithm has better performance in terms of edge preserving and denoising.Based on CUDA, the second part of this dissertation proposes a parallel algorithm to speed up the SAR image interpolation algorithm based on the piecewise autoregressive model. Due to hardware condition and SAR imaging limitation, the resolution of obtained SAR image is not high enough for observation and analysis. Common image interpolation algorithms, such as nearest neighbor, bilinear interpolation, bicubic interpolation, cubic convolution interpolation, can be used to enlarge the original image. These algorithms can help to improve the visual effect of image in some degree. But limited by the fineness of algorithms, the improvement to visual effect of image is not high enough. The piece-wise 2D autoregressive (PAR) model interpolation algorithm uses non-linear optimization method to evaluate model parameters and missing pixels simultaneously. Because of good adaptability to image local pixel structure, this algorithm can get a high image reproduction quality. But the computational cost of this algorithm is also very high. A parallel algorithm based on CUDA is proposed to speed up the image interpolation algorithm based on the piecewise autoregressive model. An image is first divided into multiple 9?9 small local windows. For each local window, first, a CUDA thread is used to interpolate the local window using the autoregressive interpolation algorithm whose parameters are found by the high accuracy gradient descent algorithm; in the second interpolation round, the gradient descent algorithm uses the estimated parameters of the autoregressive model found in the first interpolation as the initial values to decrease the computation time in the second interpolation round. Numerical simulation indicates that this GPU based parallel algorithm can interpolate a 2592?1944 image within 1/110 of the time used by a CPU based algorithm. Moreover, the computation time saved decreases with the image size. Since this algorithm can parallel process the pixel interpolation on GPU, compared with traditional serial algorithm that runs on CPU, this parallel implementation can make better use of the local property of the piecewise autoregressive model and parallel property of CUDA, eventually achieving an interpolation image of high quality in a low computation time.The third part of this dissertation, a SAR image segmentation algorithm based on multi-mode distribution is proposed. In SAR image, the generations of image, target and background clutter are different, which give rise to their different statistical characteristics. In this part, an analysis of the different imaging characteristics of the three components is made and statistical models of them are developed, on the basis of which, a multi-mode distribution SAR image segmentation algorithm based on the combination of the three models is proposed. For the target and shadows segmentation, different pre-processing methods are adopted. A fast one-dimensional Otsu algorithm is proposed to segment target from background clutter and a SAR image enhancement algorithm based on the background mean-preserving shock filter to segment shadow from background clutter. Experimental results show that, compared with traditional segmentation algorithms based on the single-mode distributions, this multi-mode distribution segmentation algorithm can make the best use of the differences of statistical characteristics of the three components, with high segmentation accuracy and a better practicability.The fourth part of this dissertation gives an SAR target recognition algorithm based on fusion of target contour and shadow contour. When performing SAR automatic target recognition, the internal structure characteristics of target are most frequently used, such as gray value, peak value and center distance. SAR image contour information is rarely used in target recognition and SAR image shadow information is even less used. Actually, SAR image contour can reflect local spatial structure information of SAR target. If SAR image is segmented appropriately, correct and fine contour information can be acquired. Contour information is a kind of stable recognition characteristic. Therefore, to acquire target contour and shadow contour, we propose an SAR image segmentation algorithm based on marker-controlled. Then, fusion of these two kinds of contour is used to perform SAR target recognition. The effectiveness of this algorithm is verified through experimental results on MSTAR data. Experimental results show that, besides local spatial structure information, fusion of target contour and shadow contour also contains height information of SAR target. Compared with these two features used independently, fusion of contour is a kind of more stable characteristic.The fifth part of this dissertion presents an estimation algorithm for SAR target attitude angle. SAR imaging is very sensitive to target azimuth angle. When relative position between SAR and target is changed, scattering center of target will chage. This causes same target varies significantly under different azimuth angle. Therefore, SAR target attitude angle estimation is an important step of SAR target recognition. During SAR target classification and recognition, precise azimuth angle estimation can help to reduce target matching number and detection error. Based on the target segmentation mentioned above, we analyze the unchanged characteristics of SAR image under different azimuth angle and propose a Hough transformation algorithm based on clear double edge. Experimental result based on MSTAR measured data show that, using this algorithm to estimate SAR target attitude angle can achieve high precision and short computation time.Based on texture characteristic, the sixth part of this dissertation gives a SAR image recognition algorithm. Aiming at SAR target variant, a SAR target variant recognition algorithm based on local texture characteristic is also included in this part. Since one target usually has multiple variants in practice, the measured data in the real world is quite different from the training data, which cause the SAR automatic target recognition (ATR) difficult and becomes one of the major factors affecting SAR target recognition rate. Therefore this paper presents a SAR ATR algorithm aiming at the target variants. The new algorithm uses the local texture similarity between the variant and the original target for recognition. Firstly, a SAR image registration algorithm based on clear edges is proposed. Then, the texture characteristic obtained by a combined use of the Gabor transform, LBP and spatial domain histogram is employed to describe the SAR image. At last, histogram sequence matching based on the large characteristic is used to perform the recognition. Since texture characteristic based on registration is used to describe the SAR image, the SAR target can be described effectively. Furthermore, local histogram matching is used to perform the recognition; therefore the new algorithm has better generalization property than algorithms based on the global characteristic. The effectiveness of the proposed algorithm is verified by experimental results on MSTAR S2.