南京大学学报（自然科学版）Issue(5)：976-986,11.DOI:10.13232/j.cnki.jnju.2015.05.008
基于马尔可夫随机场模型的 SAR 图像积雪识别
Recognizing snow cover from SAR image based on Markov Random Field model
摘要
Abstract
This study proposed Markov Random Field(MRF)to recognize snow cover using RADARSAT-2 data on 1 9 March 2014 in Manasi River Basin,Xinjiang Province.The MRF model based image segmentation method can take full advantage of the contextual information,and reduce the influence of speckle noise on SAR data.We estimated the MRF parameters following the initial k-means segmentation,then established the prior model and the probability density function.Finally,we used Iterated Conditional Model(ICM)for solving the maximum posterior probability of the optimal label to identify the snow cover.Verified by the field survey data,the accuracy of the method to recognize snow cover was 86.67%.The results showed shat the MRF model based segmentation method could effectively recognize snow cover.In the flat areas,the backscattering coefficient under the cross-polarization HV and the polarization total power Span had the good recognition accuracy.But in the mountainous areas,the recognition accuracy of the HV backscattering coefficient decreased with the increase of elevation and slope.The polarization total power Span can integrate the three polarization characteristics to overcome the topographic effect,and to impove the recognition accuracy of snow cover.关键词
马尔可夫随机场/合成孔径雷达/图像分割/积雪识别/玛纳斯河流域Key words
Markov Random Field/synthetic aperture radar/image segmentation/snow cover recognition/Manasi River Basin分类
信息技术与安全科学引用本文复制引用
周淑媛,肖鹏峰,冯学智,朱榴骏,郭金金..基于马尔可夫随机场模型的 SAR 图像积雪识别[J].南京大学学报（自然科学版）,2015,(5):976-986,11.基金项目
国家自然科学基金(41271353),国家高分辨率对地观测系统重大专项(95 Y40B02900113/1504) （41271353）
