Hyper Parameter Estimation in MRF-based SAR Chip Image Segmentation
SAR chip image segmentation is a key step in SAR automatic target recognition (ATR). Aiming at this challenging task, many available methods are proposed. Among these methods, MRF-based segmentation is the most popular one. MRF-based method for SAR chip image segmentation is more like an inverse-filter which tries to smooth noise. The hyper parameter determines its capability of noise reduction. The larger the hyper parameter, the more noise is smoothed. However, when the hyper parameter gets large, some interested regions may be regarded as noise and be smoothed. In order to smooth all noise and preserve interested regions, the hyper parameter should be carefully selected. In this paper, by rewriting the regularization term in MRF-based SAR chip image segmentation, we find its similarity with total variation (TV) filtering. Moreover, some sort of analytic expression of TV regularization hyper parameter has been derived. According to this similarity, we are convinced that these analytic results of hyper parameter estimation in TV filtering may be carefully extended to MRFbased SAR chip image segmentation. A core concept in these expressions is ‘scale’ which refers to the area-perimeter ratio of interested regions. However, when it comes to MRF-based SAR chip image segmentation, the ‘scale’ has to be redefined. In our study, we redefine the ‘scale’ and illustrate scales of some canonical shapes. Finally, these formulations of MRF hyper parameter are validated by simulated data.
SAR chip image segmentation Markov randomfield hyper parameter estimation total variation scale.
Zhang Zebing Hu Weidong
ATR Key Lab, National University of Defense Technology,Changsha, China
国际会议
2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)
成都
英文
760-763
2011-10-24（万方平台首次上网日期，不代表论文的发表时间）