基于蚁群算法和支持向量机的矿化蚀变信息提取研究
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摘要
传统地质找矿方法往往是通过地面调查来圈定蚀变带,耗费大量的人力、物力。通过遥感数据提取矿化蚀变信息,获取蚀变岩的空间分布特征,一直是遥感找矿工作的重要研究内容。但是由于矿化蚀变信息在遥感图像上是一种弱信息,使用传统的信息提取方法效果往往不尽人意。因此,研究有效的遥感矿化蚀变信息提取新技术新方法,提高遥感找矿的可信度和效益,具有非常重要的科学和现实意义。
蚁群算法(Ant colony optimization algorithm,ACO)是一种模拟自然界蚂蚁集体寻径行为的全新仿生进化算法,具有离散性、并行性、鲁棒性、正反馈性等特点。由于其概念简明、实现方便,迅速得到相关科技人员的认可。支持向量机(Support vector machine,SVM)是机器学习领域的研究热点,并在很多方面都得到了成功的应用。
结合国土资源地质大调查项目“全波段定量化遥感技术及其在资源环境调查中的应用研究”(工作项目编码:1212010660601)的子工作项目“矿产资源遥感综合信息提取技术与找矿应用研究”,以青海黄南州同仁—泽库地区作为研究区,开展了基于蚁群算法和支持向量机的矿化蚀变信息提取方法的研究。取得的主要成果如下:
(1)提出利用蚁群算法对SVM主要参数进行搜索。SVM分类器模型中参数的选取,对分类器的性能产生较大的影响,为避免传统的网格搜索参数带来的时间消耗和搜索范围难于确定问题,提出利用蚁群算法对主要参数进行搜索。通过蚁群参数优化和网格搜索参数优化的仿真实验,表明:蚁群参数优化算法比网格搜索算法能更快更优的搜索到SVM的主要参数。
(2)建立了基于主成分分析的支持向量机矿化信息提取模型。针对传统矿化信息提取方法需要大量样本,且样本选取困难的缺陷,提出利用主成分分析和支持向量机原理,建立矿化信息提取模型。既解决了SVM样本选取困难的问题,又克服了传统的统计方法只有在样本数量趋于无穷大时才能有理论上保证的缺陷,保证了矿化信息提取的精度。通过实地验证和与已知矿点叠加分析,表明该方法是一种有效的蚀变信息提取方法。
(3)提出基于纹理和光谱的SVM矿化信息提取方法。综合考虑了基于像素的光谱和基于空间特性的纹理和结构信息,充分利用了现有的遥感资料光谱分辨率和空间分辨率,克服了传统上只利用光谱或者只利用纹理,信息量相对较少的局限,保证了SVM矿化样本选取的精度。通过所提取的遥感蚀变异常信息与原有矿区叠加分析,叠加基本吻合:从野外实地验证来看,均发现了不同程度的矿化现象,并指出了3个重点异常区。
(4)提出基于蚁群算法的光谱分解方法,来剔除植被等干扰信息。首次将蚁群这种全新的算法引入到遥感地质领域。基于蚁群算法的光谱分解方法,综合考虑传统的光谱分解植被剔除方法处理速度慢和蚁群算法识别目标速度快的特点,通过残差图分析以及原图与剔除植被后影像对比分析,初步验证了基于蚁群算法的光谱分解方法来剔除植被信息的可行性。
(5)首次结合蚁群算法与遥感地质领域应用较成熟的比值方法,建立泥化蚀变信息提取模型。选取青海黄南州阿哇地区为研究区。首先确定泥化蚀变信息提取规则:然后建立基于蚁群的泥化蚀变信息提取模型;最后根据模型提取泥化蚀变信息。通过叠加分析及野外实地验证,表明:效果良好。
Mineral alteration zones are delineated by ground survey in traditional methods of geological ore-searching. Mineral alteration data are extracted and spatial distribution features of altered rocks are acquired by remote sensing information extraction ways. Although remote sensing method is superior to ground survey in traditional methods in saving manpower and resources, common remote sensing information extraction methods can't extract the mineral alteration data well because the mineral alteration data are too weak to be extracted in remote sensing data. It has great practical significance to study on finding new remote sensing altered information extraction methods in order to enhance the reliability and economic benefit of remote sensing exploration.
Ant Colony Algorithm (ACO) is a novel biomimetic algorithm which stimulates collective routing in nature and has some good features, such as discreteness, parallelism, robustness, positive feedback, etc., and is recognized and accepted rapidly for its advantages, such as explicit concepts, convenient realization. Support vector machine (SVM) is a kind of novel machine learning methods, based on statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance, and it has been applied in many domains successfully.
This paper is supported by the project: Study on the technology of remote sensing comprehensive information extraction of mineral resources and the application to ore-searching, which is the subject of the project of Great Geological Survey of Land and Resources: Study on the Technology of Whole-Band Quantitative Remote Sensing and Its Application in Resource and Environment Survey (Contract serial number: 1212010660601).Tongren-Zeku area, belong to Huangnanzhou city of Qinghai Province, was chosen as the study area, and the study of mineral alteration information was extracted based on Ant Colony Algorithm and Support vector machine.The main conclusions are listed as follows:
(1) The adopting parameters in the classifier model of SVM can make significant influence to the property of the classifier. ACO algorithm was selected to be used in the main parameters searching in order to avoid time consuming and scoping area uncertainty duo to grid searching method. ACO algorithm is more quickly and better than grid searching in finding out main parameters of SVM, through simulation test.
(2) SVM mineral information extraction model, based on principal component analysis, was established. In order to solve the defaults of traditional mineral alteration information extraction, such as large sample volume and difficulties of sample selection, a new extraction model, based on principal component analysis and SVM, is proposed in this paper. Using this new model, not only can solve the defaults of traditional extraction method, but also can ensure the extraction precise of mineral information. Through the validation on the spot and overlay analysis with known samples, it has been validated that this is an effective way to extract alteration information.
(3) The SVM mineral information extraction method, based on texture and spectra, is proposed in this paper, which both overcome the limitation of information content by combining pixel spectrum and spatial texture and structure, as well as combining spectrum resolution and spatial resolution of remote sensing data, and ensure the mineral sample precise of SVM. Overlaying the extracted remote sensing anomaly alteration information with the known mineral field, the effect is good. Through the validation on the spot, alteration phenomenon in different degrees has been found, and three key anomaly areas were pointed out.
(4) The spectral unmixing method based on ACO algorithm, which is the first time to import ACO algorithm to remote sensing geology field, is proposed to reject vegetation disturbance information. This method makes the target recognition quickly, and has been proved to be doable to reject vegetation information by error diagram analysis and contrast analysis between original map and the rejected vegetation image.
(5) The argillization alteration extraction information model, combining ACO algorithm and ratio method in traditional remote sensing geology area is firstly established. Awa area, belong to Huangnan city of Qinghai province, was selected as study area. First, the rules of hydroxyl alteration information extraction model were established. Then, hydroxyl alteration information extraction model, based on ACO algorithm, was established. Finally, hydroxyl alteration information was extracted by the model. Overlaying the extracted remote sensing anomaly alteration information with the known mineral field, the effect is good. Through the validation on the spot, alteration phenomenon in different degrees has been found.
蚁群算法(Ant colony optimization algorithm,ACO)是一种模拟自然界蚂蚁集体寻径行为的全新仿生进化算法,具有离散性、并行性、鲁棒性、正反馈性等特点。由于其概念简明、实现方便,迅速得到相关科技人员的认可。支持向量机(Support vector machine,SVM)是机器学习领域的研究热点,并在很多方面都得到了成功的应用。
结合国土资源地质大调查项目“全波段定量化遥感技术及其在资源环境调查中的应用研究”(工作项目编码:1212010660601)的子工作项目“矿产资源遥感综合信息提取技术与找矿应用研究”,以青海黄南州同仁—泽库地区作为研究区,开展了基于蚁群算法和支持向量机的矿化蚀变信息提取方法的研究。取得的主要成果如下:
(1)提出利用蚁群算法对SVM主要参数进行搜索。SVM分类器模型中参数的选取,对分类器的性能产生较大的影响,为避免传统的网格搜索参数带来的时间消耗和搜索范围难于确定问题,提出利用蚁群算法对主要参数进行搜索。通过蚁群参数优化和网格搜索参数优化的仿真实验,表明:蚁群参数优化算法比网格搜索算法能更快更优的搜索到SVM的主要参数。
(2)建立了基于主成分分析的支持向量机矿化信息提取模型。针对传统矿化信息提取方法需要大量样本,且样本选取困难的缺陷,提出利用主成分分析和支持向量机原理,建立矿化信息提取模型。既解决了SVM样本选取困难的问题,又克服了传统的统计方法只有在样本数量趋于无穷大时才能有理论上保证的缺陷,保证了矿化信息提取的精度。通过实地验证和与已知矿点叠加分析,表明该方法是一种有效的蚀变信息提取方法。
(3)提出基于纹理和光谱的SVM矿化信息提取方法。综合考虑了基于像素的光谱和基于空间特性的纹理和结构信息,充分利用了现有的遥感资料光谱分辨率和空间分辨率,克服了传统上只利用光谱或者只利用纹理,信息量相对较少的局限,保证了SVM矿化样本选取的精度。通过所提取的遥感蚀变异常信息与原有矿区叠加分析,叠加基本吻合:从野外实地验证来看,均发现了不同程度的矿化现象,并指出了3个重点异常区。
(4)提出基于蚁群算法的光谱分解方法,来剔除植被等干扰信息。首次将蚁群这种全新的算法引入到遥感地质领域。基于蚁群算法的光谱分解方法,综合考虑传统的光谱分解植被剔除方法处理速度慢和蚁群算法识别目标速度快的特点,通过残差图分析以及原图与剔除植被后影像对比分析,初步验证了基于蚁群算法的光谱分解方法来剔除植被信息的可行性。
(5)首次结合蚁群算法与遥感地质领域应用较成熟的比值方法,建立泥化蚀变信息提取模型。选取青海黄南州阿哇地区为研究区。首先确定泥化蚀变信息提取规则:然后建立基于蚁群的泥化蚀变信息提取模型;最后根据模型提取泥化蚀变信息。通过叠加分析及野外实地验证,表明:效果良好。
Mineral alteration zones are delineated by ground survey in traditional methods of geological ore-searching. Mineral alteration data are extracted and spatial distribution features of altered rocks are acquired by remote sensing information extraction ways. Although remote sensing method is superior to ground survey in traditional methods in saving manpower and resources, common remote sensing information extraction methods can't extract the mineral alteration data well because the mineral alteration data are too weak to be extracted in remote sensing data. It has great practical significance to study on finding new remote sensing altered information extraction methods in order to enhance the reliability and economic benefit of remote sensing exploration.
Ant Colony Algorithm (ACO) is a novel biomimetic algorithm which stimulates collective routing in nature and has some good features, such as discreteness, parallelism, robustness, positive feedback, etc., and is recognized and accepted rapidly for its advantages, such as explicit concepts, convenient realization. Support vector machine (SVM) is a kind of novel machine learning methods, based on statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance, and it has been applied in many domains successfully.
This paper is supported by the project: Study on the technology of remote sensing comprehensive information extraction of mineral resources and the application to ore-searching, which is the subject of the project of Great Geological Survey of Land and Resources: Study on the Technology of Whole-Band Quantitative Remote Sensing and Its Application in Resource and Environment Survey (Contract serial number: 1212010660601).Tongren-Zeku area, belong to Huangnanzhou city of Qinghai Province, was chosen as the study area, and the study of mineral alteration information was extracted based on Ant Colony Algorithm and Support vector machine.The main conclusions are listed as follows:
(1) The adopting parameters in the classifier model of SVM can make significant influence to the property of the classifier. ACO algorithm was selected to be used in the main parameters searching in order to avoid time consuming and scoping area uncertainty duo to grid searching method. ACO algorithm is more quickly and better than grid searching in finding out main parameters of SVM, through simulation test.
(2) SVM mineral information extraction model, based on principal component analysis, was established. In order to solve the defaults of traditional mineral alteration information extraction, such as large sample volume and difficulties of sample selection, a new extraction model, based on principal component analysis and SVM, is proposed in this paper. Using this new model, not only can solve the defaults of traditional extraction method, but also can ensure the extraction precise of mineral information. Through the validation on the spot and overlay analysis with known samples, it has been validated that this is an effective way to extract alteration information.
(3) The SVM mineral information extraction method, based on texture and spectra, is proposed in this paper, which both overcome the limitation of information content by combining pixel spectrum and spatial texture and structure, as well as combining spectrum resolution and spatial resolution of remote sensing data, and ensure the mineral sample precise of SVM. Overlaying the extracted remote sensing anomaly alteration information with the known mineral field, the effect is good. Through the validation on the spot, alteration phenomenon in different degrees has been found, and three key anomaly areas were pointed out.
(4) The spectral unmixing method based on ACO algorithm, which is the first time to import ACO algorithm to remote sensing geology field, is proposed to reject vegetation disturbance information. This method makes the target recognition quickly, and has been proved to be doable to reject vegetation information by error diagram analysis and contrast analysis between original map and the rejected vegetation image.
(5) The argillization alteration extraction information model, combining ACO algorithm and ratio method in traditional remote sensing geology area is firstly established. Awa area, belong to Huangnan city of Qinghai province, was selected as study area. First, the rules of hydroxyl alteration information extraction model were established. Then, hydroxyl alteration information extraction model, based on ACO algorithm, was established. Finally, hydroxyl alteration information was extracted by the model. Overlaying the extracted remote sensing anomaly alteration information with the known mineral field, the effect is good. Through the validation on the spot, alteration phenomenon in different degrees has been found.
引文
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