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多角度多波段的核函数及其在BRDF研究中的应用
引用本文:刘思含,刘强,柳钦火,李小文.多角度多波段的核函数及其在BRDF研究中的应用[J].北京师范大学学报(自然科学版),2007,43(3):309-313.
作者姓名:刘思含  刘强  柳钦火  李小文
作者单位:1. 中国科学院遥感应用研究所,遥感科学国家重点实验室,100101,北京
2. 中国科学院遥感应用研究所,遥感科学国家重点实验室,100101,北京;北京师范大学地理学与遥感科学学院,遥感科学国家重点实验室,环境遥感与数字城市北京市重点实验室,100875,北京
基金项目:国家自然科学基金,中国科学院知识创新工程项目
摘    要:利用叶片与土壤光谱的先验知识,在核驱动模型的基础上将基于几何学学的LiSparse核与RossThick体散射核改写为角度和波长的共同函数,以核函数的加权来描述亚像元与像元的关系,从而使核系数成为与波长无关而只与冠层结构相关的参数,并基于此模型提出了新的宽波段反照率的新算法.利用波谱数据库收录的冬小麦观层多角度多波段数据进行核系数的反演,证明了在观测角度较少的条件下,用多波段数据联合反演核系数要比用单一波段数据反演核系数的结果更为稳定.

关 键 词:多角度  多波段  核驱动模型  反照率
修稿时间:2007-02-11

MULTI-ANGLE AND MULTI-SPECTRUM KERNEL-DRIVEN MODEL AND ITS APPLICATION IN THE RESEARCH OF BRDF
Liu Sihan,Liu Qiang,Liu Qinhuo,Li Xiaowen.MULTI-ANGLE AND MULTI-SPECTRUM KERNEL-DRIVEN MODEL AND ITS APPLICATION IN THE RESEARCH OF BRDF[J].Journal of Beijing Normal University(Natural Science),2007,43(3):309-313.
Authors:Liu Sihan  Liu Qiang  Liu Qinhuo  Li Xiaowen
Institution:1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, 100101, Beijing, China; 2.Sehool of Geography and Remote Sensing Science, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Seiences; Beijing Key Laboratory of Enviromental Remote Sensing and City Digitalization, Beijing Normal University; 100875, Beijing, China
Abstract:With the support of spectrum database and adding the wavelength as a variable,the prior knowledge of the soil and leaf spectrum is used to derive the new expresstion of the LiSparse kernel and the RossThick kernel.The weight of kernels describes the relationship between the pixel and sub pixel,and can be used to derive short wave albedo.The kernel weight retrieval with the BRDF of winter wheat canopy provided by the Spectrum Database System of Typical Objects in China are done.Results show that the weights of the kernels are solely related with the canopy structure,and the inversion results with multi-band data are much more stable than that with single band data when the number of angular samples are limited.
Keywords:multi-angle  multi-spectral  kernel-driven model  albedo
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