地表参量遥感反演理论与方法研究
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摘要
前向建模和基于模型的反演是定量遥感的两个主要方向。近几十年来,各国科学家在前向建模领域进行了大量的工作,提出了几百种植被与光辐射模型。而遥感模型的反演研究只是在近几年才引起科学家们较为广泛的关注。相对于前向建模而言,遥感模型的反演尚处在探索阶段,尤其在先验知识的应用、反演策略的制定和反演算法上均面临许多困难.高光谱、多角度遥感传感器的出现,丰富了遥感探测手段,为我们提供了更为丰富的光谱维、空间维信息,也对定量遥感提出越来越高的要求。如何充分利用新型遥感传感器为我们提供的种种便利,解决以前定量遥感中无法解决甚至无法想象的问题,使遥感真正进入定量化时代,是遥感科学家们面临的一项艰巨而紧迫的任务。
本研究即是在上述背景下产生,其主要目的在于进一步完善现有反演理论,并在此基础上,探索高光谱、多角度联合反演地表参数的途径及可行性。
在反演方面,主要分析了模型反演的误差来源、指出了先验信息、代价函数的定义形式、以及反演算法、反演策略对反演结果的影响,在此基础上,对基于不确定性和敏感性矩阵的多阶段反演策略进行改进,使其更加适合于不确定性反演。
对于非线性遥感模型反演来说,反演算法对结果的影响不应忽略,然而在目前的遥感反演研究中,反演算法的影响很少被考虑。事实上,非线性优化问题很早就引起了人们的兴趣,但是对于这类问题,至今尚无很好的解决办法。遥感反演中对于非线性反演问题一般采用确定性搜索算法来解决。近年来,随着计算技术的发展,一些新的智能算法(如模拟退火算法,模拟进化算法等)得到了迅速发展和广泛应用。为非线性优化问题的解决提供了一条新途径。本文在综合考虑各种算法的优缺点基础上,将基于实编码的遗传算法引入遥感模型反演中,并通过与确定性搜索算法相比较,肯定了遗传算法在解决非线性反演问题中的作用。
陆地遥感反演本质上是一个病态反演问题,在反演过程中必须尽可能利用先验知识。本文探讨了从图像本身获取先验信息以及高光谱、多角度结合反演地表参量的可行性,提出了高光谱、多角度结合反演地表参量的思路与方法,并尝试采用GOMS 模型和线性光谱混合模型反演相结合,利用BOREAS 数据提取像元组份高光谱和结构参数以及植冠覆盖度等信息。
本研究主要有以下创新:
提出广义遥感反演与狭义遥感反演以及类内混合像元分解与类间混合像元分解的概念,明确所研究内容的内涵和外延。
从理论上分析了遥感反演的误差来源,并分别加以分析,给出相应的解决方案。
对基于不确定性和敏感性矩阵的多阶段反演策略进行了改进.采用使不敏感参数按其先验分布随机变化而不是固定在先验值的方法,以获得待反演参数的不确定性。
Both modeling and model-based inversion are important for quantitative remote sensing. Hundreds of models related to vegetation and radiation have been established during the past decade years. However, the model-based inversion caused the attentions of scientists only in recent years. Compared to modeling, model-based inversion is still in the stage of exploration. Lots of difficulties exist in the application of a priori information, inverse strategy and inverse algorithm. The appearance of hyperspectral and multiangular remote sensor enhanced the exploration means, and provided us more spectral and spatial dimension information than before. How to utilize these information to solve the problems faced in quantitative remote sensing to make remote sensing really enter the time of quantification is an arduous and urgent task for remote sensing scientists. Remote sensing inversion is paid more and more attentions in recent years. In a series of international study projections, such as IGBP, WCRP and EOS, remote sensing inversion is included as a focal point of study.
In China, scientists, represented by Prof. Li X.W., not only have made great achievements in forward modeling, but also realized the importance of remote sensing inversion and studied it as a special direction early. They've done lots of creative work for remote sensing inversion.
This study arises from the above background. Its main aim is to perfect current inverse theory. On this basis, exploring the method and feasibility to extract terrestrial parameters from hyperspectral and multiangular data.
On the inverse problem, we analyzed the error source of model-based inversion, and pointed out the effect of a priori information, merit function, inverse algorithm and strategy on the inverse result. On this basis, Improving the Bayes inversion and the USM-based multistage inverse strategy to make them adapt to uncertain inversion. At the same time, we introduced the real-valued Genetic Algorithm (GA) to remote sensing inversion, and compared it with deterministic searching method. The result shows that GA functions well for the nonlinear inverse problem.
In this dissertation, we put forward the idea and method to combine hyperspectral and multiangular data together to extract terrestrial parameters, and tried to inverse component signatures and structure parameters from Boreas data using GOMS model and linear spectral unmixing model.
The dissertation has following special points:
1. Putting forward the concept of broad sense and narrow sense remote sensing inversion and the concept of subpixel unmixing between classes and within classes. 2. Analyzing the error source of remote sensing inversion, and providing the corresponding solution scheme. 3. Improving the USM-based multistage inverse strategy. Using the method to vary insensitivity parameters according to their priori distributions instead of fixing them to priori values to acquire the uncertainties of parameters to be inverted. 4. Suggesting paying some attentions to the constraint conditions for uncertain inversion to make them suitable to circumstances where models or observed data are not certain enough. 5. Introducing real-valued GA to remote sensing model inversion, and improving the objectivity and global convergence of traditional GA from some aspects, such as the initial population, the crossover rate, etc. 6. Comparing GA with general deterministic searching method, analyzing the efficiency and deficiency of GA, and approving the validity of GA in resolving the problem of nonlinear model inversion. On this basis, putting forward the mixed GA method. 7. Presenting the idea to extract terrestrial parameters from hyperspectral and multiangular data together, and studying its validity. 8. Studying the method to acquire and express a priori information, and probe the possibility to extract a priori information from remote sensing images directly. 9. Deducing the formula to calculate crown cover projection(CCP) based on Geometrical-Optical model.
本研究即是在上述背景下产生,其主要目的在于进一步完善现有反演理论,并在此基础上,探索高光谱、多角度联合反演地表参数的途径及可行性。
在反演方面,主要分析了模型反演的误差来源、指出了先验信息、代价函数的定义形式、以及反演算法、反演策略对反演结果的影响,在此基础上,对基于不确定性和敏感性矩阵的多阶段反演策略进行改进,使其更加适合于不确定性反演。
对于非线性遥感模型反演来说,反演算法对结果的影响不应忽略,然而在目前的遥感反演研究中,反演算法的影响很少被考虑。事实上,非线性优化问题很早就引起了人们的兴趣,但是对于这类问题,至今尚无很好的解决办法。遥感反演中对于非线性反演问题一般采用确定性搜索算法来解决。近年来,随着计算技术的发展,一些新的智能算法(如模拟退火算法,模拟进化算法等)得到了迅速发展和广泛应用。为非线性优化问题的解决提供了一条新途径。本文在综合考虑各种算法的优缺点基础上,将基于实编码的遗传算法引入遥感模型反演中,并通过与确定性搜索算法相比较,肯定了遗传算法在解决非线性反演问题中的作用。
陆地遥感反演本质上是一个病态反演问题,在反演过程中必须尽可能利用先验知识。本文探讨了从图像本身获取先验信息以及高光谱、多角度结合反演地表参量的可行性,提出了高光谱、多角度结合反演地表参量的思路与方法,并尝试采用GOMS 模型和线性光谱混合模型反演相结合,利用BOREAS 数据提取像元组份高光谱和结构参数以及植冠覆盖度等信息。
本研究主要有以下创新:
提出广义遥感反演与狭义遥感反演以及类内混合像元分解与类间混合像元分解的概念,明确所研究内容的内涵和外延。
从理论上分析了遥感反演的误差来源,并分别加以分析,给出相应的解决方案。
对基于不确定性和敏感性矩阵的多阶段反演策略进行了改进.采用使不敏感参数按其先验分布随机变化而不是固定在先验值的方法,以获得待反演参数的不确定性。
Both modeling and model-based inversion are important for quantitative remote sensing. Hundreds of models related to vegetation and radiation have been established during the past decade years. However, the model-based inversion caused the attentions of scientists only in recent years. Compared to modeling, model-based inversion is still in the stage of exploration. Lots of difficulties exist in the application of a priori information, inverse strategy and inverse algorithm. The appearance of hyperspectral and multiangular remote sensor enhanced the exploration means, and provided us more spectral and spatial dimension information than before. How to utilize these information to solve the problems faced in quantitative remote sensing to make remote sensing really enter the time of quantification is an arduous and urgent task for remote sensing scientists. Remote sensing inversion is paid more and more attentions in recent years. In a series of international study projections, such as IGBP, WCRP and EOS, remote sensing inversion is included as a focal point of study.
In China, scientists, represented by Prof. Li X.W., not only have made great achievements in forward modeling, but also realized the importance of remote sensing inversion and studied it as a special direction early. They've done lots of creative work for remote sensing inversion.
This study arises from the above background. Its main aim is to perfect current inverse theory. On this basis, exploring the method and feasibility to extract terrestrial parameters from hyperspectral and multiangular data.
On the inverse problem, we analyzed the error source of model-based inversion, and pointed out the effect of a priori information, merit function, inverse algorithm and strategy on the inverse result. On this basis, Improving the Bayes inversion and the USM-based multistage inverse strategy to make them adapt to uncertain inversion. At the same time, we introduced the real-valued Genetic Algorithm (GA) to remote sensing inversion, and compared it with deterministic searching method. The result shows that GA functions well for the nonlinear inverse problem.
In this dissertation, we put forward the idea and method to combine hyperspectral and multiangular data together to extract terrestrial parameters, and tried to inverse component signatures and structure parameters from Boreas data using GOMS model and linear spectral unmixing model.
The dissertation has following special points:
1. Putting forward the concept of broad sense and narrow sense remote sensing inversion and the concept of subpixel unmixing between classes and within classes. 2. Analyzing the error source of remote sensing inversion, and providing the corresponding solution scheme. 3. Improving the USM-based multistage inverse strategy. Using the method to vary insensitivity parameters according to their priori distributions instead of fixing them to priori values to acquire the uncertainties of parameters to be inverted. 4. Suggesting paying some attentions to the constraint conditions for uncertain inversion to make them suitable to circumstances where models or observed data are not certain enough. 5. Introducing real-valued GA to remote sensing model inversion, and improving the objectivity and global convergence of traditional GA from some aspects, such as the initial population, the crossover rate, etc. 6. Comparing GA with general deterministic searching method, analyzing the efficiency and deficiency of GA, and approving the validity of GA in resolving the problem of nonlinear model inversion. On this basis, putting forward the mixed GA method. 7. Presenting the idea to extract terrestrial parameters from hyperspectral and multiangular data together, and studying its validity. 8. Studying the method to acquire and express a priori information, and probe the possibility to extract a priori information from remote sensing images directly. 9. Deducing the formula to calculate crown cover projection(CCP) based on Geometrical-Optical model.
引文
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