云模型支持下的遥感图像分类粒计算方法研究
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摘要
遥感图像分类就是依据遥感图像的光谱、纹理、形状等特征将图像划分为互不相交的地物类型区域的过程,是遥感图像地物提取的前提和基础,分类结果的好坏直接影响到后续处理的精度。遥感图像分类是遥感信息提取技术中的一个经典难题,也是公认的一个具有挑战性的研究领域。分析和总结遥感图像的特点及近年来遥感图像分类方法的发展趋势,可以发现,粒度问题和不确定性问题是影响遥感图像分类结果的两个关键问题。
粒度原本是一个物理学概念,用来度量微粒的平均大小,粒计算理论借用了这一概念,用来实现对认知过程中的概念或知识在不同层次或不同角度上的度量。粒计算理论模拟人类从不同层次,不同角度观察和处理问题的策略,根据需要在不同的粒度空间内求解问题,并且对计算中数据的不确定性具有很强的适应能力。粗糙集、商空间、模糊信息粒等理论和方法都相继在粒计算理论下进行了自身的延拓和扩展。目前,粒计算发展成为信息处理的一种新概念、新方法,覆盖了所有和粒度相关的理论、方法和技术,主要用于描述和处理模糊的、随机的、不完整的和海量的信息及提供一种基于粒和粒间关系的问题求解方法。
本文通过对粒计算的基本原理、方法和常用粒计算模型的研究和分析,将云模型纳入到粒计算的理论体系中；从粒化、粒层次构建、粒的计算三个方面构建基于云模型的粒计算方法的体系和框架,针对其中的关键问题和技术进行了研究。将其应用于遥感图像分类中,提出了基于云模型的遥感图像粒计算分类方法,分别针对中低分辨率遥感图像和高分辨率遥感图像进行分类实验,与传统的最大似然、最小距离、马式距离和支持向量机等遥感图像分类算法进行对比实验,实验结果表明该方法具有较高的分类精度,验证了方法的有效性。
本文的主要工作和创新点如下：
(1)将云模型纳入到粒计算的理论体系中,提出了基于云模型的粒计算方法。分别从粒化、粒层次构建和粒计算三个方面阐述了基于云模型的粒计算的体系结构和框架,并将其应用于遥感图像分类中,提出了基于云模型的遥感图像分类的粒计算方法。
(2)提出了三种粒化方法：基于直方图分析的云变换粒化方法、基于高斯混合模型的云变换粒化方法、基于云模型的区域粒化方法。
针对启发式云变换不能计算超熵和使用隶属曲线拟合频率分布曲线的缺陷,提出了一种基于直方图分析的云变换粒化方法；针对遥感图像的多波段特点和启发式云变换不能处理高维数据的缺陷,提出了一种基于高斯混合模型的云变换粒化方法；针对高分辨遥感图像空间信息和结构信息丰富的特点,提出了一种基于云模型的遥感图像区域粒化方法。
(3)改进了粒层次构建的三个关键技术：云距离计算、云综合和粒层次构建的终止条件。借鉴模糊集合的距离计算方法提出了一种云距离计算方法,通过云质心距离和云贴近度实现云距离的计算,基于云模型的期望曲线提出了一种简化计算方法,实现了云距离的快速计算。针对传统积分云综合只能实现抽象概念间综合的缺陷,提出了一种幅度云综合算法应用于像素集合间的合并。使用传统积分云综合算法构建粒层次结构不会出现雾化现象,粒层次结构构建的终止条件是顶层云模型的个数为1,使用幅度云综合算法构建粒层次结构会出现雾化现象,提出了一种基于云贴近度的粒层次构建的终止条件,避免粒层次构建过程中出现雾化现象。
(4)提出了两种基于云模型的遥感图像分类方法：基于云模型和模糊模式识别的中低分辨率遥感图像分类方法、基于云模型和模糊规则推理的高分辨率遥感图像分类方法。针对中低分辨率遥感图像,利用基于个体识别的最大隶属度判定方法实现遥感图像的非监督分类；将粒层次结构中的云模型作为激发云模型,通过训练样本生成判别云模型,利用模糊综合评判实现遥感图像的监督分类。针对高分辨率遥感图像,对图像进行区域粒化,然后提取区域特征,构建区域的特征空间,然后在特征空间内,分别针对满足正态分布的特征和不满足正态分布的特征使用模糊综合评判和基于AdaBoost算法的模糊规则推理,从而实现高分辨率遥感图像图像的分类。
Remote sensing image classification is the image classification processing based on the spectral, texture, shape and other characteristics of the remote sensing image. It is the premise and basis of remote sensing image information extraction. Classification results have a direct impact on the accuracy of subsequent processing. Remote sensing image classification is a classic problem of remote sensing information extraction, and it is recognized as a challenging area of research. It can be found that granularity and uncertainty are the two key issues which affect the remote sensing image classification results by summary of the characteristics of remote sensing and improvements of the classification method in recent years.
Granularity used to be a physics concept which is used to measure the average size of particle. Granular computing theory borrows the concept which is used to measure the concept or knowledge at different levels and different perspectives. Theory of granular computing simulates problem solving strategies of human which deal with problems in different granularity space. It has a strong ability to quickly deal with the uncertainty of data. Rough sets, quotient space and fuzzy information granularity theory have been improved and developed under the granularity theory. At present, granular computing is a new concept and method of information processing including all relevant theories, methods and techniques of granularity theory. It is mainly used to describe and deal with fuzzy, random, incomplete, and large amounts of information. It provides a kind of problem-solving methods based on granular computing and researchs of relationship between granularities.
Based on the research and analysis of basic principles, methods and commonly used model of granular computing, the paper introduces cloud model into the theory systems of granular computing. The framework is built from granulating, granular layer construction and granular computing. The key issues and techniques are discussed and improved. For remote sensing image classification, the method of classification based cloud model is proposed. The low resolution remote sensing images and high resolution remote sensing image classification experiments verify the effectiveness of the method.
The main work and innovation are as follows:
(1) The paper introduces cloud model into the theory systems of granular computing. The framework is built from granulating, granular layer construction and granular computing, and the application to remote sensing image classification is proposed.
(2) Cloud transform algorithm is improved. Heuristic cloud transform algorithm can not calculate He and it uses the membership curve to fit the frequency distribution curve. In order to overcome drawbacks, a cloud transform algorithm based on histogram analysis is proposed. A cloud transform algorithm based on Gaussian mixture model is presented to realize granulating of the multi-dimension data. For dealing with the spatial and structure information of the high resolution remote sensing image, the region segmentation method based on cloud model is proposed to realize region granulating of the high resolution remote sensing image.
(3) Three key technologies of granular layer construction cloud model distance calculation, cloud synthesis and termination condition of granular layer construction are improved. A new cloud model distance calculation method is proposed based on fuzzy degree of nearness, and the amplitude cloud synthesis is introduced, termination condition of granular layer construction is used to avoid cloud model atomizing utilizing cloud model distance calculation and amplitude cloud synthesis algorithm.
(4) Classification method based on cloud model and fuzzy pattern recognition is proposed for the low resolution remote sensing images classification, and classification method based on cloud model and fuzzy rule reasoning is presented for the high resolution remote sensing images classification.
粒度原本是一个物理学概念,用来度量微粒的平均大小,粒计算理论借用了这一概念,用来实现对认知过程中的概念或知识在不同层次或不同角度上的度量。粒计算理论模拟人类从不同层次,不同角度观察和处理问题的策略,根据需要在不同的粒度空间内求解问题,并且对计算中数据的不确定性具有很强的适应能力。粗糙集、商空间、模糊信息粒等理论和方法都相继在粒计算理论下进行了自身的延拓和扩展。目前,粒计算发展成为信息处理的一种新概念、新方法,覆盖了所有和粒度相关的理论、方法和技术,主要用于描述和处理模糊的、随机的、不完整的和海量的信息及提供一种基于粒和粒间关系的问题求解方法。
本文通过对粒计算的基本原理、方法和常用粒计算模型的研究和分析,将云模型纳入到粒计算的理论体系中；从粒化、粒层次构建、粒的计算三个方面构建基于云模型的粒计算方法的体系和框架,针对其中的关键问题和技术进行了研究。将其应用于遥感图像分类中,提出了基于云模型的遥感图像粒计算分类方法,分别针对中低分辨率遥感图像和高分辨率遥感图像进行分类实验,与传统的最大似然、最小距离、马式距离和支持向量机等遥感图像分类算法进行对比实验,实验结果表明该方法具有较高的分类精度,验证了方法的有效性。
本文的主要工作和创新点如下：
(1)将云模型纳入到粒计算的理论体系中,提出了基于云模型的粒计算方法。分别从粒化、粒层次构建和粒计算三个方面阐述了基于云模型的粒计算的体系结构和框架,并将其应用于遥感图像分类中,提出了基于云模型的遥感图像分类的粒计算方法。
(2)提出了三种粒化方法：基于直方图分析的云变换粒化方法、基于高斯混合模型的云变换粒化方法、基于云模型的区域粒化方法。
针对启发式云变换不能计算超熵和使用隶属曲线拟合频率分布曲线的缺陷,提出了一种基于直方图分析的云变换粒化方法；针对遥感图像的多波段特点和启发式云变换不能处理高维数据的缺陷,提出了一种基于高斯混合模型的云变换粒化方法；针对高分辨遥感图像空间信息和结构信息丰富的特点,提出了一种基于云模型的遥感图像区域粒化方法。
(3)改进了粒层次构建的三个关键技术：云距离计算、云综合和粒层次构建的终止条件。借鉴模糊集合的距离计算方法提出了一种云距离计算方法,通过云质心距离和云贴近度实现云距离的计算,基于云模型的期望曲线提出了一种简化计算方法,实现了云距离的快速计算。针对传统积分云综合只能实现抽象概念间综合的缺陷,提出了一种幅度云综合算法应用于像素集合间的合并。使用传统积分云综合算法构建粒层次结构不会出现雾化现象,粒层次结构构建的终止条件是顶层云模型的个数为1,使用幅度云综合算法构建粒层次结构会出现雾化现象,提出了一种基于云贴近度的粒层次构建的终止条件,避免粒层次构建过程中出现雾化现象。
(4)提出了两种基于云模型的遥感图像分类方法：基于云模型和模糊模式识别的中低分辨率遥感图像分类方法、基于云模型和模糊规则推理的高分辨率遥感图像分类方法。针对中低分辨率遥感图像,利用基于个体识别的最大隶属度判定方法实现遥感图像的非监督分类；将粒层次结构中的云模型作为激发云模型,通过训练样本生成判别云模型,利用模糊综合评判实现遥感图像的监督分类。针对高分辨率遥感图像,对图像进行区域粒化,然后提取区域特征,构建区域的特征空间,然后在特征空间内,分别针对满足正态分布的特征和不满足正态分布的特征使用模糊综合评判和基于AdaBoost算法的模糊规则推理,从而实现高分辨率遥感图像图像的分类。
Remote sensing image classification is the image classification processing based on the spectral, texture, shape and other characteristics of the remote sensing image. It is the premise and basis of remote sensing image information extraction. Classification results have a direct impact on the accuracy of subsequent processing. Remote sensing image classification is a classic problem of remote sensing information extraction, and it is recognized as a challenging area of research. It can be found that granularity and uncertainty are the two key issues which affect the remote sensing image classification results by summary of the characteristics of remote sensing and improvements of the classification method in recent years.
Granularity used to be a physics concept which is used to measure the average size of particle. Granular computing theory borrows the concept which is used to measure the concept or knowledge at different levels and different perspectives. Theory of granular computing simulates problem solving strategies of human which deal with problems in different granularity space. It has a strong ability to quickly deal with the uncertainty of data. Rough sets, quotient space and fuzzy information granularity theory have been improved and developed under the granularity theory. At present, granular computing is a new concept and method of information processing including all relevant theories, methods and techniques of granularity theory. It is mainly used to describe and deal with fuzzy, random, incomplete, and large amounts of information. It provides a kind of problem-solving methods based on granular computing and researchs of relationship between granularities.
Based on the research and analysis of basic principles, methods and commonly used model of granular computing, the paper introduces cloud model into the theory systems of granular computing. The framework is built from granulating, granular layer construction and granular computing. The key issues and techniques are discussed and improved. For remote sensing image classification, the method of classification based cloud model is proposed. The low resolution remote sensing images and high resolution remote sensing image classification experiments verify the effectiveness of the method.
The main work and innovation are as follows:
(1) The paper introduces cloud model into the theory systems of granular computing. The framework is built from granulating, granular layer construction and granular computing, and the application to remote sensing image classification is proposed.
(2) Cloud transform algorithm is improved. Heuristic cloud transform algorithm can not calculate He and it uses the membership curve to fit the frequency distribution curve. In order to overcome drawbacks, a cloud transform algorithm based on histogram analysis is proposed. A cloud transform algorithm based on Gaussian mixture model is presented to realize granulating of the multi-dimension data. For dealing with the spatial and structure information of the high resolution remote sensing image, the region segmentation method based on cloud model is proposed to realize region granulating of the high resolution remote sensing image.
(3) Three key technologies of granular layer construction cloud model distance calculation, cloud synthesis and termination condition of granular layer construction are improved. A new cloud model distance calculation method is proposed based on fuzzy degree of nearness, and the amplitude cloud synthesis is introduced, termination condition of granular layer construction is used to avoid cloud model atomizing utilizing cloud model distance calculation and amplitude cloud synthesis algorithm.
(4) Classification method based on cloud model and fuzzy pattern recognition is proposed for the low resolution remote sensing images classification, and classification method based on cloud model and fuzzy rule reasoning is presented for the high resolution remote sensing images classification.
引文
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