A precise and fast SAR image segmentation method

作者：Ju Yanwei^*; Tian Zheng; Zhang Yan

来源：Chinese Journal of Electronics, 2007, 16(3): 471-475.

• generalized multiresolution likelihood ratio (GMLR)
• bootstrap sampling
• spatially variant mixture multiscale autoregressive prediction (SVMMARP) model
• maximization likelihood estimation
• SAR image
• unsupervised segmentation

A Generalized multiresolution likelihood ratio (GMLR), which can increases the distinction between different signals by fusing their more features, is defined. The GMLR for SAR (Synthetic aperture radar) image, the features of it which produced by the pyramid representation of SAR imagery characterizes and exploits the multiscale stochastic structure inherent in SAR imagery due to radar speckle. In our unsupervised SAR image segmentation method, a Spatially variant mixture multiscale autoregressive prediction (SVMMARP) model is proposed to estimate the parameters of GMLR based on maximum likelihood estimation. In order to satisfy the independence assumption of maximum likelihood estimation and reduce the segmentation time greatly, we perform our method based on the Bootstrap sampling technique. The algorithm avoids some drawbacks that existed in some popular segmentation techniques. Experimental results demonstrate that our algorithm performs fairly well.

• 出版日期2007-7
• 单位西北工业大学