偏振云检测中基于多目标优化的角度选择研究
投稿时间:2019-06-17  修订日期:2020-04-15  点此下载全文
引用本文:方薇,乔延利,张冬英,杜丽丽,易维宁.偏振云检测中基于多目标优化的角度选择研究[J].红外与毫米波学报,2020,39(3):339~347].FANG Wei,QIAO Yan-Li,ZHANG Dong-Ying,DU Li-Li,YI Wei-Ning.Angle selection research based on multi-objectives optimized detection of clouds[J].J.Infrared Millim.Waves,2020,39(3):339~347.]
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方薇 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 fwei@aiofm.ac.cn 
乔延利 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  
张冬英 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  
杜丽丽 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 lilydu@aiofm.ac.cn 
易维宁 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  
基金项目:国家自然科学基金 41601379;高分辨对地观测系统重大专项(民用部分)项目 32-Y20A17-9001-15/17 30-Y20A010-9007-17/18国家自然科学基金(41601379)、高分辨对地观测系统重大专项(民用部分)项目(32-Y20A17-9001-15/17,30-Y20A010-9007-17/18)
中文摘要:多角度信息在扩展云检测功能、提高检测精度等优势的同时,带来多角度遍历计算的复杂度和数据规模增大的问题。云检测角度信息来自地表的二向性反射分布函数和大气分子散射效应,尽管在局部区域内存在用分析解确定某些观察角的可能性,但由于卫星运动观察几何变化、云几何因子的影响、地物调查的巨大工作量等实际应用的复杂性,POLDER等官方产品仍采用所有角度遍历计算。由于邻近角度间信息的冗余,文章用平均联合信息熵和K-L信息散度作为角度子集选择的特征,提出了Pareto多目标前沿最优解和理想解算法,在POLDER和“高分五号”卫星搭载的多角度偏振探测仪(directional polarimetric camera, DPC)的数据集上进行云检测实验。2角度组合结果与POLDER产品相比,总体精度89.36%,Kappa系数0.7845,DPC检测分类相似度86%,时间复杂度减少约1/7。实验表明所提方法在保持检测效果的同时具有降低计算开销的优点,可为云检测提供一种快速有效、满意精度和自动化运行的新途径。
中文关键词:云检测  多角度偏振遥感  遥感角度选择  Pareto最优
 
Angle selection research based on multi-objectives optimized detection of clouds
Abstract:It brings about both higher computational complexity and data scale augmentation, while multi-directional information of remote sensing images has the superiority in extending function and increasing accuracy in cloud detection. Directonal information on cloud detection comes from BRDF of surface combined with atmospheric effects. Although determing certain view angles in local area by analytic solution is possible, but considering the complexities of application the traverse calculation for whole angles is still carried out in POLDER officer products. Due to redundant information existed on close neighbours of angler layer, averaged joint information entropy and K-L information divergence formed a feature basis for selection of angle subset. Two algorithms of optimal and idel solutions on Pareto multi-objectives front are proposed. Experiments of cloud detection were taken on two POLDER datasets firstly, then on a dataset of directional polarimetric camera (DPC) on board GF-5 satellite. The experimental results demonstrated that the overall accuracy of cloud detection by proposed method based on 2 angle- layers combinations is 89.36%, Kappa equals 0.7845 The validation on DPC dataset also showed that in comparison with GF-5 remote sensing synthetic image the similarity among them is 86%. The computation efficiency was raised to 7 times . Thus the proposed method takes advantages on computational cost saving and retaining multi-angle image’s intrinsic information. It contributes a new insight to cloud detection with its advantages of effectiveness, satisfactory accuracy and automatic operation.
keywords:cloud detection  multi-angular polarized remote sensing  angular selection in remote sensing  Pareto Optimality
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