基于模式识别的激光雷达遥感灰霾组分识别模型
发布时间：2018-10-22 19:26
【摘要】：提出了一种基于贝叶斯模式识别的激光雷达大气遥感灰霾组分识别的方法。介绍了灰霾组分模式识别模型的建立过程,并利用具体的贝叶斯判别函数作为灰霾粒子光学特征向量的选择依据对灰霾粒子进行识别分类。采用计算机仿真实现了该灰霾组分模式识别模型,并通过两种自验证方法检验了模型的正确性和稳定性。讨论了该模型对现有大气遥感激光雷达的适用性,凸显了偏振高光谱分辨率激光雷达(HSRL)的优势。
[Abstract]:A method based on Bayesian pattern recognition (Bayesian pattern recognition) for atmospheric remote sensing of haze components in lidar is proposed. This paper introduces the establishment of haze component pattern recognition model and classifies haze particles according to the selection of optical eigenvector of haze particles by using specific Bayesian discriminant function (Bayesian discriminant function). The model of haze component pattern recognition is realized by computer simulation, and the correctness and stability of the model are verified by two self-verification methods. The applicability of the model to the existing atmospheric remote sensing lidar is discussed, which highlights the advantages of polarization hyperspectral resolution lidar (HSRL).
【作者单位】： 浙江大学光电信息工程学系现代光学仪器国家重点实验室;
【基金】：国家自然科学基金(41305014) 教育部博士点基金(20130101120133) 浙江省教育厅科研项目(Y201329660) 浙江省“仪器科学与技术”重中之重学科开放基金(JL130113) 中央高校基本科研业务费专项资金(2013QNA5006) 现代光学仪器国家重点实验室创新基金(MOI201208)
【分类号】：TN958.98
[Abstract]:A method based on Bayesian pattern recognition (Bayesian pattern recognition) for atmospheric remote sensing of haze components in lidar is proposed. This paper introduces the establishment of haze component pattern recognition model and classifies haze particles according to the selection of optical eigenvector of haze particles by using specific Bayesian discriminant function (Bayesian discriminant function). The model of haze component pattern recognition is realized by computer simulation, and the correctness and stability of the model are verified by two self-verification methods. The applicability of the model to the existing atmospheric remote sensing lidar is discussed, which highlights the advantages of polarization hyperspectral resolution lidar (HSRL).
【作者单位】： 浙江大学光电信息工程学系现代光学仪器国家重点实验室;
【基金】：国家自然科学基金(41305014) 教育部博士点基金(20130101120133) 浙江省教育厅科研项目(Y201329660) 浙江省“仪器科学与技术”重中之重学科开放基金(JL130113) 中央高校基本科研业务费专项资金(2013QNA5006) 现代光学仪器国家重点实验室创新基金(MOI201208)
【分类号】：TN958.98
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