偏振多通道遥感云检测的阈值优化  

Threshold Optimization in Cloud Detection byPolarized Multichannel Remote Sensing

作　　者：方薇 乔延利[1] 张冬英[1] 易维宁[1] Fang Wei;Qiao Yanli;Zhang Dongying;Yi Weining(Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Sciences,Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Hefei,Anhui 230031,China;University of Science and Technology of China,Hefei,Anhui 230026,China)

机构地区：[1]中国科学院安徽光学精密机械研究所中国科学院通用光学定标与表征技术重点实验室,安徽合肥230031 [2]中国科学技术大学,安徽合肥230026

出　　处：《光学学报》2018年第12期378-385,共8页Acta Optica Sinica

基　　金：国家国防科工局高分专项(民用部分)卫星应用共性关键技术项目(32-Y20A22-9001-15/17);中国科学院重点资助项目(KGFZD-125-13-006)

摘　　要：大气中云的存在会严重影响气溶胶的反演精度。经验阈值法是一种常用的云检测方法,其较强的主观性和难以应对环境时空动态变化或星载探测仪差异的缺点,导致"云"和"晴"边缘分类误差增大,且检测自动化程度较低。针对下垫面为陆地的大气云检测,提出一种多通道偏振遥感图像的统计分类与数据融合的阈值优化方法,该方法首先通过半监督Kmeans聚类及其统计特征,决定像元属于"云"和"晴"两类的双亮度阈值;然后在阈值周边分类模糊区,用D-S证据理论获取多通道检测的联合置信度因子,求得模糊区像元分类的细化阈值;最终以顺序决策过程实现"云"和"晴"两类目标的精确分类。为了验证所提方法的有效性,利用POLDER3载荷遥感数据进行云检测实验,并与POLDER3产品结果进行比较。结果表明:所提方法与POLDER法的分类符合度为95%,目测发现这些误检大多发生在云边缘处,表明所提方法对云边缘处的分类具有较好的敏感性。The existence of clouds in the atmosphere degrades the accuracy of aerosol retrieval. The empirical threshold method is popular in could detection, however its strong subjectivity and difficulty in coping with the dynamic spatial-temporal changes of the environment or the difference among satellite-borne detectors result in a large classification error at the boundary of ‘cloud’ and ‘clear’. In addition, its automatic detection is also poor. To achieve an effective detection of cloud over the land surface in the atmosphere, we propose a threshold optimized method which combines the statistical classification with data fusion of polarized multichannel remote sensing images. As for this method, a dual-brightness threshold to distinguish ‘cloud’ from ‘clear’ for most pixels is first derived based on the semi-supervised Kmeans clustering and its statistical features. Then, the joint confidence factor of multichannel data is calculated by the D-S evidence theory for each pixel in the fuzzy area of threshold neighborhood, and thus the fine threshold is acquired. The two objects of ‘cloud’ and ‘clear’ are finally and accurately classified in the sequential decision process. To validate the effectiveness of the proposed method, we perform a cloud detection experiment based on the remote sensing load data of POLRED3, and compare the measured results with the results of POLRED3. The results show that the classification by the proposed method is well consistent with that by the POLDER method with a high conformity of 95%. The error pixels are mostly located at the boundary between cloud and clear, indicating that the proposed method exhibits a favorable sensitivity to the classification at the cloud edge.

关 键 词：遥感 阈值优化 偏振遥感 云检测 自适应阈值 D-S证据理论 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP7[自动化与计算机技术—计算机科学与技术]