摘要
针对多极化合成孔径雷达(polarimetric synthetic aperture radar,POLSAR)影像由于受到相干斑噪声影响导致分类精度较低,提出了一种基于均值漂移和多尺度马尔科夫随机场的非监督分类算法。该算法首先由Mean-Shift算法得到最粗尺度的初始分类结果,然后由马尔科夫随机场对结果进行优化得到最粗尺度最终分类结果。将上一尺度的分类结果映射到下一尺度作为初始分类结果,然后由Wishart分布对极化协方差矩阵进行建模并采用迭代条件模式(iterative conditional modes,ICM)算法求取基于最大后验下分类结果。逐层映射,最细尺度的结果作为最终分类结果。详细给出了算法的基本原理和实施步骤,并采用E-SAR和AirSAR数据对算法进行了验证。实验表明,与同类算法相比较,算法具有更高的分类精度。
For reducing the impact caused by speckle noise on classification results,a new classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on mean-shift and multiscale Wishart Markov random field. The mean-shift algorithm is used to get the initial classification for the coarsest scale, and then a Markov random field is introduced to achieve the classification result. The classification result on a coarser scale is employed as the initial classification of the nearest finer scale. Meanwhile, the Wishart distribution is employed to model the observed field, and then the iterative conditional modes （ICM） algorithm is adopted to implement the maximum a posteriori estimation of pixel labels for each scale. The classification result of the finest scale is the final classification result for POLSAR images. The algorithm is described in detail and the eontrastive experiment is done using AirSAR L band and E-SAR polarimetric images. The experiment result indicates that the proposed method could get higher accuracy of classification than the classical algorithms.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第11期2413-2417,共5页
Systems Engineering and Electronics
基金
国家自然科学基金(61001187
41001256
40971219
60602013)
国家高技术研究发展计划(863计划)(2007AA120204)资助课题
关键词
多极化合成孔径雷达
分类
均值漂移
马尔科夫随机场
WISHART分布
polarimetric synthetic aperture radar （POLSAR）
classification
mean shift
Markov random field （MRF）
Wishart distribution
