Detection And Discrimination Of The Vehicle In SAR Imagery

Detection and recognition of vehicles in a SAR image is an important task for acquirement of information from the battlefield, and is also a concerned difficult problem in SAR image interpretation. The problem is usually divided into three stages: detection, discrimination and classification. In the first stage, the observed data are scanned quickly to acquire the vehicles' ROIs, and these ROIs are distinguished in the second stage to remove the false ROIs by the features of vehicle. And then, the only target-like ROIs are sent to the computationally expensive classification stage. However, it is almost improbable to recognize automatically those vehicles based on the existing technology, such as the theory of electromagnetic scattering. Therefore, it is significant to research further the detection and discrimination of vehicles, so that we can find quickly the targets and calculate their orientations from a large scene.In order to detect and discriminate the vehicles from the large complex observed scene, some key technologies including the CFAR detection, region segmentation, feature extraction and target discrimination are studied systematically in this thesis. And we can use these technologies to form a framework to find quickly the vehicle from the test SAR image and calculate its orientation.The CFAR detecting algorithm for vehicle based on ODVI-AC in a nonhomogeneous SAR image is developed in chapter 2. Firstly, the method of counting the threshold of the ODVI-AC is proposed. Using this method, we can calculate the adaptive thresholds of automatic censoring process. After removing the pixels of strong clutter and interfering targets in the reference window of a test cell by an ODVI-AC algorithm, the remaining pixels are used to estimate the parameters of statistical model. Adopting a two-parameter CFAR detector and Gauss distribution, we calculate the testing statistic and its threshold in CFAR to fulfill an adaptive CFAR detection of the vehicle.The region segmentation of vehicle ROI and clustering method are discussed in chapter 3. The methods of SAR image segmentation are summarized. And three algorithms, including OS-CFAR, MAP based on MRF and MAP based on the modified P-M model, are used to carry out the region segmentation of vehicle, respectively. Because a vehicle may be separated into several isolated regions, it is necessary to use the space clustering algorithm and morphologic operators, such as connection, filling, and eliminating, to filter the regions. So, we can have the whole region of the vehicle and calculate its center coordinate.Features for vehicle discrimination, including geometry features, scattering features, multiresolution discriminant feature, are summed up in chapter 4, and the methods of extracting those features are also presented. At the same time, the method of minimum enclosing rectangle and the Radon transform are introduced to estimate the length and width of the vehicle in single polarimetric SAR image. According to the different back scattering characteristic between the vehicle and natural terrain, the lacunarity feature is developed. Moreover, it is concluded that lacunarity is robust to the additional noise and the speckle noise.For reducing the dimension of discriminating feature space, the method of selecting the optimal features for target discrimination is proposed in chapter 5. The redundancy, robustness and separability of features are quantitatively analyzed by this method. Based on the optimal features, the multi-features sequential discrimination is developed and compared with the quadratic distance discriminating method. In addition, useing those key technologies in this thesis, we form the framework of acquiring the vehicle's ROI in a high resolution SAR image.