首页> 外文会议>European Signal Processing Conference(EUSIPCO 2005); 20050904-08; Antalya(TK) >EXTRACTION OF HYDROGRAPHIC NETWORKS FROM SATELLITE IMAGES USING A HIERARCHICAL MODEL WITHIN A STOCHASTIC GEOMETRY FRAMEWORK
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EXTRACTION OF HYDROGRAPHIC NETWORKS FROM SATELLITE IMAGES USING A HIERARCHICAL MODEL WITHIN A STOCHASTIC GEOMETRY FRAMEWORK

机译:随机几何框架内使用分层模型从卫星图像中提取水文网络

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This article presents a two-step algorithm performing an un-supervised extraction of hydrographic networks from satellite images, within a stochastic geometry framework. First, the thick branches of the network are detected by a segmentation algorithm based on a Markov random field. Second, the line branches of the network are extracted using a recursive algorithm based on a hierarchical model of hydrographic network, in which the tributaries of a given river are modeled by an object process in the neighborhood of this river. Optimization of the object process is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. We show experimental results on a satellite radar image.
机译:本文介绍了一种两步算法,可在随机几何框架内从卫星图像执行无监督水文网络提取。首先,通过基于马尔可夫随机场的分段算法来检测网络的粗支路。其次,使用基于水文网络分层模型的递归算法提取网络的支线,其中,给定河流的支流通过该河流附近的对象过程进行建模。通过使用可逆跳跃马尔可夫链蒙特卡洛算法进行模拟退火,可以完成对象过程的优化。我们在卫星雷达图像上显示实验结果。

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