基于网格和智能算法的遥感岩性分类方法研究
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摘要
在遥感岩性分类的过程中,海量的遥感数据和面向应用的快速处理需求,需要强大的计算资源支持。而当研究区域从实验点增大到区域性范围时,现有的普通单机计算能力已远达不到时效性的需求。
当前根据谱带强度的统计特征进行岩性分类的算法,在形成分类算子时需要对波段进行统计,而波段数的增加必然导致算法处理速度变慢。该缺陷决定了其应用范围常限于多光谱遥感,在高光谱中很难推广使用。而基于波型特征的岩性分类算法,在分类时也同样遇到因计算速度变慢而导致处理效率低的问题。
如何解决因海量数据或算法本身而导致的此类问题,目前大部分的研究都集中在算法的优化上,而鲜有对高性能计算优化方法进行研究。本论文的着眼点和出发点在于：同时在算法和高性能计算两个方面对遥感岩性分类进行性能优化研究,并最终提出一种新的基于服务的网格遥感岩性分类模式。论文的主要贡献如下：
(1)针对大区域遥感岩性分类计算量大、处理时间长、效率低的问题,提出了基于网格环境的遥感岩性分类应用模型。该模型支持分布式并行运算和集成,支持面向服务的模式以响应用户应用需求,支持按步骤、按过程和按数据三种方式对分类过程中的密集型计算进行分解部署。该模型的提出,在计算性能的层面,弥补了岩性分类过程中对海量数据处理计算能力的不足,并能满足各个应用层面对计算性能的高要求。
(2)针对传统统计学的遥感岩性分类方法在样本有限的情况下难以取得理想分类精度的问题,提出了基于感兴趣区域的SVM岩性分类方法。该方法以二叉树的SVM分类算法为基础,通过参照已有区域地质资料,分别选取感兴趣区域一定数量的具有普遍性、代表性的不同类型岩石样本,再以各类样本在对应波段的波谱反射率生成各岩石类别的参考训练样本,进行训练和分类。该分类方法的应用消除了因样本不足对分类精度的影响。
(3)针对SVM分类过程中,处理大规模训练样本集遇到的因样本维度高、消耗大量内存导致分类效率低下的问题,提出了基于PSO算法对训练样本集进行缩减的策略。该策略以k-折交叉验证作为算法的适应度评价机制。此策略的提出和应用,得到了训练样本中有效支持向量的最小化组合,提高了训练和分类速度,在算法的层面弥补了处理大规模数据对计算性能的高要求。
(4)针对传统岩性分类方法其分类结果,过分依赖训练样本及因人为失误或偏差导致分类实效下降的问题,提出了网格环境下基于多种群协同进化粒子群算法的岩性分类模型。该模型以光谱相似尺度方法构建分类规则,种群的多样性依托于网格环境,将计算分布并行处理。这种分类方法的提出是统计学分类方法的有效补充,提高了效率,降低了人为因素对分类质量的影响。
(5)针对目前大量PC资源被闲置、已有历史遥感地质数据和岩性分类相关资料没有实现充分共享,导致资源浪费和重复建设的现状,提出了面向服务的分布式并行网格岩性分类框架,并对网格服务进行了语义层次的扩展和改进。该框架的提出实现了对闲散资源的有效利用,扩展后的网格服务更有利于被网格用户发现和使用。
在研究的过程中,针对上述各种算法和模型在青海阿尔金成矿带的三个研究区域(采石场、阿卡腾能山和小赛什腾)进行了遥感数据预处理、样本缩减、岩性分类等应用及大量的实验验证,并给出了该研究区域遥感岩性分类自动解译图和综合解译图。实验结果表明,本文所提出的基于网格和智能算法的遥感岩性分类方法,节约了大量处理时间,提高了处理效率和分类准确度,优化了岩性分类性能,为实现更大区域岩性分类的并行处理和资源共享奠定了理论和实践的基础。
The rapid process of mass data and requirement of orient applications need the support of powerful computation resource during the lithological discrimination of remote sensing, when the study area expands from the experimental points to the region, the existing PC computing power is far less than the requirement of time efficiency.
Currently, most of lithological discrimination algorithms are based on statistical characteristics of multi-band intensity, these algorithms need to statistic bands before classification. With the increasing of the bands, algorithm processing speed will be slow. The defect induces that it is often applied to multi-spectral remote sensing, instead of high-resolution spectral remote sensing. The lithological characteristics of wave-based discrimination algorithms, in the discrimination are also inefficient due to slow calculation speed.
At present, how to deal with the problem of slow processing speed and efficiency caused by the massive data or algorithm itself, most studies have focused on the optimization algorithm instead of optimization methods of high-performance calculation. This paper puts forward a distributed parallel computing environment by grid technique, and intelligent algorithms, and proposes a new discrimination method of lithology. Finally, a new service-based grid lithological discrimination model of remote sensing is proposed. The main contributions of this paper are as follows:
(1) In order to resolve the problems of the remote sensing lithological discrimination, such as the massive calculation, long processing time and low efficiency, a grid-based lithological discrimination application model of remote sensing is proposed. The model supports distributed parallel computing and integration, supports service-oriented model in response to user application requirements and supports decomposition of calculation-intensive in the course of large-scale remote sensing lithological discrimination by step, process and data. The model makes up the deficiencies of the massive computing power lithological discrimination processing, and can meet the high demand of computing performance on each individual application.
(2)In the traditional statistical discrimination of remote sensing lithology, limited samples are difficult to obtain good discrimination accuracy, so, the remote sensing lithology discrimination method is presented for the interest region based on support vector machines binary decision tree algorithm. Referring to the existing regional geological data, a certain number of universal, representative of the different types of rock samples are selected in the interest regions area. and then, various types of samples in the corresponding band Spectral reflectance of various rock types generated training samples, Participate in training and discrimination. The method eliminates the impact of discrimination accuracy because of insufficient sample.
(3)Typical computational problems, such as consuming large amounts of memory due to high sample dimensions during large-scale training sets SVM discrimination, are overcome for the first time with the strategy of reducing large-scale SVM training samples based on PSO. In order to decrease the dimension of the discrimination computation, a training model is constructed based on the resulting samples. A fast, efficient and accurate discrimination is established in combination of using K-fold cross validation as the fitness evaluation mechanism. That gives an effective optimization method for extracting lithological information from large areas by using large-scale training sets.
(4) To overcome the problem of the lithological discrimination of traditional over-reliance on training samples, and decreased effectiveness of discrimination results in human error or bias, a feasibility analysis on the parallel computation realization of the large-scale lithological discrimination based on PSO is performed. A discussion referring to realizing the multi-PSO lithological discrimination is made. The flow, result and evaluating method of discrimination are also given in this paper. The discrimination of rocks, combined with the computational efficiency of analysis and visual interpretation of the accuracy compared to verify the model using lithological discrimination, this discrimination method proposed based on multi-PSO and grid is an effective supplement of statistical discrimination methods, it can reduce the computation time and reduce man-made factors on the quality of discrimination.
(5) In order to make use of idle PC resources, available remote sensing of geological data and lithological discrimination documents, avoid waste of resources and duplication, a service-based distributed parallel lithological discrimination framework is proposed, the grid computational-resource sharing mode based on the syntax and service quality combined extension is also presented. It achieves the efficient usage of idle resources, the extension of the grid service makes it easy to be found and be used by the grid users.
In this paper, remote sensing lithological discrimination workflow is established based on the grid computation and the above-mentioned algorithms. Preprocessing remote sensing data, sample reduction and lithological discrimination are applied to several typical targeted areas. Through applying the united parallel processing to the Arkin metallogenetic zone, experiments results demonstrate the new method can save processing time, improve efficiency and discrimination accuracy. This method also provides the theoretical and practical base for the united parallel processing of lithological discrimination in the whole district.
当前根据谱带强度的统计特征进行岩性分类的算法,在形成分类算子时需要对波段进行统计,而波段数的增加必然导致算法处理速度变慢。该缺陷决定了其应用范围常限于多光谱遥感,在高光谱中很难推广使用。而基于波型特征的岩性分类算法,在分类时也同样遇到因计算速度变慢而导致处理效率低的问题。
如何解决因海量数据或算法本身而导致的此类问题,目前大部分的研究都集中在算法的优化上,而鲜有对高性能计算优化方法进行研究。本论文的着眼点和出发点在于：同时在算法和高性能计算两个方面对遥感岩性分类进行性能优化研究,并最终提出一种新的基于服务的网格遥感岩性分类模式。论文的主要贡献如下：
(1)针对大区域遥感岩性分类计算量大、处理时间长、效率低的问题,提出了基于网格环境的遥感岩性分类应用模型。该模型支持分布式并行运算和集成,支持面向服务的模式以响应用户应用需求,支持按步骤、按过程和按数据三种方式对分类过程中的密集型计算进行分解部署。该模型的提出,在计算性能的层面,弥补了岩性分类过程中对海量数据处理计算能力的不足,并能满足各个应用层面对计算性能的高要求。
(2)针对传统统计学的遥感岩性分类方法在样本有限的情况下难以取得理想分类精度的问题,提出了基于感兴趣区域的SVM岩性分类方法。该方法以二叉树的SVM分类算法为基础,通过参照已有区域地质资料,分别选取感兴趣区域一定数量的具有普遍性、代表性的不同类型岩石样本,再以各类样本在对应波段的波谱反射率生成各岩石类别的参考训练样本,进行训练和分类。该分类方法的应用消除了因样本不足对分类精度的影响。
(3)针对SVM分类过程中,处理大规模训练样本集遇到的因样本维度高、消耗大量内存导致分类效率低下的问题,提出了基于PSO算法对训练样本集进行缩减的策略。该策略以k-折交叉验证作为算法的适应度评价机制。此策略的提出和应用,得到了训练样本中有效支持向量的最小化组合,提高了训练和分类速度,在算法的层面弥补了处理大规模数据对计算性能的高要求。
(4)针对传统岩性分类方法其分类结果,过分依赖训练样本及因人为失误或偏差导致分类实效下降的问题,提出了网格环境下基于多种群协同进化粒子群算法的岩性分类模型。该模型以光谱相似尺度方法构建分类规则,种群的多样性依托于网格环境,将计算分布并行处理。这种分类方法的提出是统计学分类方法的有效补充,提高了效率,降低了人为因素对分类质量的影响。
(5)针对目前大量PC资源被闲置、已有历史遥感地质数据和岩性分类相关资料没有实现充分共享,导致资源浪费和重复建设的现状,提出了面向服务的分布式并行网格岩性分类框架,并对网格服务进行了语义层次的扩展和改进。该框架的提出实现了对闲散资源的有效利用,扩展后的网格服务更有利于被网格用户发现和使用。
在研究的过程中,针对上述各种算法和模型在青海阿尔金成矿带的三个研究区域(采石场、阿卡腾能山和小赛什腾)进行了遥感数据预处理、样本缩减、岩性分类等应用及大量的实验验证,并给出了该研究区域遥感岩性分类自动解译图和综合解译图。实验结果表明,本文所提出的基于网格和智能算法的遥感岩性分类方法,节约了大量处理时间,提高了处理效率和分类准确度,优化了岩性分类性能,为实现更大区域岩性分类的并行处理和资源共享奠定了理论和实践的基础。
The rapid process of mass data and requirement of orient applications need the support of powerful computation resource during the lithological discrimination of remote sensing, when the study area expands from the experimental points to the region, the existing PC computing power is far less than the requirement of time efficiency.
Currently, most of lithological discrimination algorithms are based on statistical characteristics of multi-band intensity, these algorithms need to statistic bands before classification. With the increasing of the bands, algorithm processing speed will be slow. The defect induces that it is often applied to multi-spectral remote sensing, instead of high-resolution spectral remote sensing. The lithological characteristics of wave-based discrimination algorithms, in the discrimination are also inefficient due to slow calculation speed.
At present, how to deal with the problem of slow processing speed and efficiency caused by the massive data or algorithm itself, most studies have focused on the optimization algorithm instead of optimization methods of high-performance calculation. This paper puts forward a distributed parallel computing environment by grid technique, and intelligent algorithms, and proposes a new discrimination method of lithology. Finally, a new service-based grid lithological discrimination model of remote sensing is proposed. The main contributions of this paper are as follows:
(1) In order to resolve the problems of the remote sensing lithological discrimination, such as the massive calculation, long processing time and low efficiency, a grid-based lithological discrimination application model of remote sensing is proposed. The model supports distributed parallel computing and integration, supports service-oriented model in response to user application requirements and supports decomposition of calculation-intensive in the course of large-scale remote sensing lithological discrimination by step, process and data. The model makes up the deficiencies of the massive computing power lithological discrimination processing, and can meet the high demand of computing performance on each individual application.
(2)In the traditional statistical discrimination of remote sensing lithology, limited samples are difficult to obtain good discrimination accuracy, so, the remote sensing lithology discrimination method is presented for the interest region based on support vector machines binary decision tree algorithm. Referring to the existing regional geological data, a certain number of universal, representative of the different types of rock samples are selected in the interest regions area. and then, various types of samples in the corresponding band Spectral reflectance of various rock types generated training samples, Participate in training and discrimination. The method eliminates the impact of discrimination accuracy because of insufficient sample.
(3)Typical computational problems, such as consuming large amounts of memory due to high sample dimensions during large-scale training sets SVM discrimination, are overcome for the first time with the strategy of reducing large-scale SVM training samples based on PSO. In order to decrease the dimension of the discrimination computation, a training model is constructed based on the resulting samples. A fast, efficient and accurate discrimination is established in combination of using K-fold cross validation as the fitness evaluation mechanism. That gives an effective optimization method for extracting lithological information from large areas by using large-scale training sets.
(4) To overcome the problem of the lithological discrimination of traditional over-reliance on training samples, and decreased effectiveness of discrimination results in human error or bias, a feasibility analysis on the parallel computation realization of the large-scale lithological discrimination based on PSO is performed. A discussion referring to realizing the multi-PSO lithological discrimination is made. The flow, result and evaluating method of discrimination are also given in this paper. The discrimination of rocks, combined with the computational efficiency of analysis and visual interpretation of the accuracy compared to verify the model using lithological discrimination, this discrimination method proposed based on multi-PSO and grid is an effective supplement of statistical discrimination methods, it can reduce the computation time and reduce man-made factors on the quality of discrimination.
(5) In order to make use of idle PC resources, available remote sensing of geological data and lithological discrimination documents, avoid waste of resources and duplication, a service-based distributed parallel lithological discrimination framework is proposed, the grid computational-resource sharing mode based on the syntax and service quality combined extension is also presented. It achieves the efficient usage of idle resources, the extension of the grid service makes it easy to be found and be used by the grid users.
In this paper, remote sensing lithological discrimination workflow is established based on the grid computation and the above-mentioned algorithms. Preprocessing remote sensing data, sample reduction and lithological discrimination are applied to several typical targeted areas. Through applying the united parallel processing to the Arkin metallogenetic zone, experiments results demonstrate the new method can save processing time, improve efficiency and discrimination accuracy. This method also provides the theoretical and practical base for the united parallel processing of lithological discrimination in the whole district.
引文
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