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COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES
SAR Image Segmentation Segmentation with Unknown Number of Classes Regular Tessellation RJMCMC Algorithm
2016/11/23
This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJ...
EXTRACTION OF FAC ADES USING RJMCMC AND CONSTRAINT EQUATIONS
Markov Chain constraint equations facade modelling building extraction least squares adjustment
2015/8/31
Today’s processes to extract man-made objects from measurement data are quite traditional. Often, they are still point based, with the exception of a few systems which allow to automatically fit simpl...
EXTRACTION OF FAC¸ ADES USING RJMCMC AND CONSTRAINT EQUATIONS
Markov Chain constraint equations fac赂ade modelling building extraction least squares adjustment
2015/8/28
Today’s processes to extract man-made objects from measurement data are quite traditional. Often, they are still point based, with the exception of a few systems which allow to automatically fit simpl...
该文基于被动多传感器阵列，在可逆跳转马尔可夫链蒙特卡罗方法基础上引入随机游走抽样理论，提出一种混合RJMCMC方法，通过将局部采样与全空间采样相结合，可以在更短的时间内得到更好地服从目标分布的随机数，结合信号和噪声的统计特性以及贝叶斯参数估计理论可实现宽带信号源数目和波达方向联合估计。仿真结果证明，该文方法能更为快速、准确地估计出信号源个数和波达方向。