The Study Of Ground Target Detection Based On Airborne-SAR Images

As an active detector, Synthetic Aperture Radar (SAR) which provides high-resolution images in all-weather, all day and all night is widely used in military reconnaissance and civil field. But as a result of the charasteristics of coherent imaging, the speckle is inevitably appeared in the SAR images and makes the processing, analysis and translation of SAR images extremely difficult. And, due to the extensive, complex and dynamic environments, the SAR image backscatter is confused and its data are so redundant. Therefore, how to synthesize various information and get a tradeoff of performances between the practicability, robustness, quickness, effectivity and accuracy of autonomous target detection have become more difficulty and critical. In this thesis, techniques for ground target detection and recognition in high resolution airborne-SAR images are extensively investigated. The main contributions of this dissertation are as follows:1, The statistical distribution model and their application of the coherent clutter in SAR images are detailedly analysized and investigated. The characteristics of several kinds of widespreadly typical non-Gaussian distribution models, such as the Gamma distribution, the Weibull distribution, the K-distribution, the finite Gaussian mixture distribution and the finite Gamma mixture distribution, are extensively discussed and studied. Then the formulae of estimating parameters' vector of these models are deduced. Last, using the MSE criterion, we analysis and estimate the statistical models(such as the Gamma distribution, the Weibull distribution and the finite Gaussian mixture distribution) of some different imaging high-resolution T72 SAR image data, and apply finite Gaussian mixture distribution to analyse statistical models of airborne synthetic aperture radar movie of Sandia National Laboratories solar power tower, movie made from multiple aspect images.2, Based on the statistical models of SAR image data, techniques for extracting target information from SAR images are probed. We proposed the local window-based Constant False Alarm Rate(CFAR), Maximum a Posteriori (MAP) and context-based methods. All these methods show the capability of extracting target...