共查询到19条相似文献，搜索用时 156 毫秒
1.
遥感岩性制图是地质填图中的重要工作,基于光谱特征的岩性分类易受到色调、纹理等因素影响导致精度不佳。前人进行岩性自动分类研究多关注影像的光谱特征,而忽略空间特征,笔者等基于甘肃北山白峡尼山地区ASTER影像,将支持向量机、极限学习机两种机器学习分类方法与基于空间特征的快速漂移算法相结合进行岩性分类。结果表明支持向量机分类总体精度为89.17%;极限学习机不但具有需调节参数少的优势,且分类精度和速度均优于支持向量机,分类总体精度达96.70%;利用快速漂移算法提取的影像空间特征可有效减少错分区,提升岩性分类效果。研究证实将基于光谱特征的极限学习机和基于空间特征的快速漂移算法结合的岩性分类方法具有客观、高效、高精度等优势,可为后续地质填图和找矿勘查工作提供可靠数据支撑,在遥感岩性分类领域具有较高的推广价值。 相似文献
2.
遥感岩性制图是地质填图中的重要工作,基于光谱特征的岩性分类易受到色调、纹理等因素影响导致精度不佳。前人进行岩性自动分类研究多关注影像的光谱特征,而忽略空间特征,笔者等基于甘肃北山白峡尼山地区ASTER影像,将支持向量机、极限学习机两种机器学习分类方法与基于空间特征的快速漂移算法相结合进行岩性分类。结果表明支持向量机分类总体精度为89. 17%;极限学习机不但具有需调节参数少的优势,且分类精度和速度均优于支持向量机,分类总体精度达96. 70%;利用快速漂移算法提取的影像空间特征可有效减少错分区,提升岩性分类效果。研究证实将基于光谱特征的极限学习机和基于空间特征的快速漂移算法结合的岩性分类方法具有客观、高效、高精度等优势,可为后续地质填图和找矿勘查工作提供可靠数据支撑,在遥感岩性分类领域具有较高的推广价值。 相似文献
3.
现行的遥感影像解译方法有监督分类和非监督分类。在监督分类中有平行算法,最小距离算法、最大似然算法等,而支持向量机是监督分类中的一种新的算法。本研究选择贵阳市花溪区小碧乡局部地区为研究对象,采用SPOT数据,分别运用最大似然算法和支持向量机算法对研究区遥感影像进行解译。通过建立混淆矩阵,来计算分类精度和Kappa系数。结果表明:支持向量机具有分类精度高,分类图斑完整等优点;但在时间的消耗上,支持向量机算法要比最大似然算法长。对于这两种算法而言,都存在地物光谱特征明显相异的地物易于区别,光谱相似的地物容易造成错分的现象,然而支持向量机分类精度要比最大似然分类精度高一些。支持向量机对样本数量具有敏感性,样本数量过多将导致运算时间过长。因此在实际运用中应根据实际情况,选择适合的算法。 相似文献
4.
基于随机森林模型的城市不透水面提取研究 ——以呼和浩特市为例 总被引：1，自引：0，他引：1
城市不透水面信息对于城市生态环境动态演化过程研究具有重要意义。以Landsat 8遥感影像为数据源,以呼和浩特市为实证区域,进行了随机森林模型应用于城市不透水面的提取研究,并与目前应用广泛的支持向量机模型进行了对比分析。研究表明：在不同的抽样比例训练样本条件下,随机森林模型对于城市不透水面的提取精度均优于支持向量机的提取精度;对于随机森林模型和支持向量机模型,70%的训练样本比例均为最佳训练样本抽样比例。在该抽样比例下,随机森林模型提取城市不透水面的总体分类精度为93.29%,Kappa系数为0.9051,支持向量机模型的总体分类精度为91.26%,Kappa系数为0.8757;随机森林模型对于城市裸土的识别度较高,能更好地将城市裸土和不透水面进行区分,而支持向量机模型对于城市裸土、不透水面和绿地的区分能力均弱于随机森林模型。综合而言,随机森林模型对城市不透水面的提取精度优于支持向量机模型,随机森林模型可以有效应用于城市不透水面提取领域,进一步丰富了城市不透水面提取方法体系构成。 相似文献
5.
《矿物岩石地球化学通报》2018,(6)
对覆盖区下伏岩体的有效识别是实现深部找矿突破的关键,近年来机器学习理论的发展为岩性识别提供了新的思路。梯度提升决策树(GBDT)算法是以决策树为基函数的集成学习方法,算法通过将学习得到的多个树模型进行集成,可以达到同时减少模型方差和偏差的效果。本文以地球化学元素数据为基础,利用GBDT算法进行岩性识别研究,并将所得结果与KNN、SVM和决策树3种机器学习算法进行对比,结果表明,针对岩性识别问题,GBDT算法具有更高的精度,可以作为岩性识别的参考技术,具有一定的推广和应用价值。 相似文献
6.
将诱发水库地震的主要因素（岩性、岩体完整性、断层性质、库区区域应力状态、库区地震活动背景）划分为11个因子,并进行定量化;再根据每个样本到所属类内超平面的距离计算每个样本点的模糊因子,确定其对分类超平面影响大小;然后建立水库地震的支持向量机（SVM）和模糊支持向量机（FSVM）模型,并应用于水库诱发地震等级预测。实例分析表明,两种模型均可用于水库诱发地震等级预测,具有预测精度较高、考虑因素全面的特点,相比之下SVM模型预测结果略优于FSVM模型。另外,在应用SVM和FSVM进行分类时,如果样本离散性较高,则SVM模型优于FSVM模型;相反,如果样本离散性较低,则FSVM模型优于SVM模型。 相似文献
7.
岩爆分类的支持向量机方法 总被引：14，自引：0，他引：14
针对岩爆分类问题,提出了基于支持向量机的分类方法。通过对影响岩爆因素的分析,运用支持向量机理论建立岩爆类别的支持向量机模型。结果表明,基于支持向量机的岩爆分类方法具有较高的准确率,该方法是科学可行的,具有广泛的应用前景。 相似文献
8.
砂砾质松散沉积物粒径组成复杂,钻探取心率低,导致对沉积相和相界面的判别准确度不高。本文结合岩心和测井资料,建立了松散沉积层基于支持向量机的测井数据岩性识别模型,分析了训练集样本数量对模型识别准确率的影响,并与BP神经网络模型进行了对比。结果表明,支持向量机模型的岩性识别准确率高,且对训练样本需求量低,可以有效地弥补钻孔取心率不足的问题,并降低钻探施工成本。在松散沉积物调查中,利用基于支持向量机的测井岩性识别模型自动识别沉积序列具有可行性,是实现绿色勘查的有益尝试。 相似文献
9.
《物探与化探》2021,(3)
煤体结构作为煤层勘探开发研究的重点参数之一,影响着煤层产能,有效识别煤层煤体结构至关重要。本文利用支持向量机算法,以地球物理测井资料为基础进行煤体结构识别,并以沁水煤田柿庄北区3号层为例,对该区块进行煤体结构类型分类,利用支持向量机的双二分类与"一对多"分类两种建模模式,建立基于测井曲线的煤体结构识别模型,再利用交叉验证评价模型的泛化性,并对该模型用未参与建模数据进行准确性评价。结果表明,应用支持向量机算法的两种模式能有效识别煤体结构,模型具有泛化性与准确性,且"一对多"分类模式精度更高,在对有利产出煤和不利产出煤的区分上效果突出,对有利产出煤的具体类型区分上具有准确性,可对后续压裂施工提供指导。总体上,基于支持向量机算法和地球物理测井资料建立的煤体结构识别模型对煤层气勘探开发有指导意义,具有实际应用价值。 相似文献
10.
《物探化探计算技术》2019,(6)
为构建适用于海洋水质的评价模型,这里以大数据下的机器学习理论为基础,利用一个包含40万个站位组成,每个站位包含13个水质指标信息的理论假设大样本,选择三种适用于多特征多分类问题的机器学习算法:决策树、贝叶斯和支持向量机来构建适用于水质评价的模型,得出基于支持向量机算法的评价模型效果最好,可作为解决利用多水质指标信息综合评价海洋水质这一问题的有效方法。 相似文献
11.
针对救援提升车结构复杂、工况故障数据独立性差、故障诊断难的特点,提出一种基于谱聚类的半监督支持向量机救援提升车故障诊断算法。该算法利用谱聚类的思想挖掘原始故障数据的隐藏特征信息,有效区分不同耦合程度的部件系统中故障信息的独立结构特征。首先根据原始输入数据建立故障图谱,然后通过建立拉普拉斯矩阵获取更加符合聚类假设的核函数,最后,建立半监督支持向量机模型,利用梯度下降算法求解最终分类结果。将上述算法应用于XCA30＿JY救援提升车工况故障诊断系统,通过搭建真实仿真环境,对采集到的工况数据进行分类,获取最终分类效果。为评价其性能,分别与传统支持向量机及梯度下降半监督支持向量机进行比较。实验结果表明：提出的算法对于救援提升车故障诊断具有较好的分类效果,救援提升车工况故障分类错误率降低至10.2%,可有效解决复杂工况故障诊断难题。由于本算法具备任意样本空间聚类及非凸函数优化求解能力,因此,除可广泛应用于车载故障诊断系统外,对于数据分类、模式识别等方面具有普遍的应用和指导意义。 相似文献
12.
13.
针对低孔、低渗致密储层识别较常规储层难这一问题,首次应用核主成分分析与支持向量机(KPCA-SVM)模型进行储层识别.该模型先通过核主成分分析(KPCA)进行非线性特征参数提取,然后将提取的特征参数作为支持向量机(SVM)的输入变量,最终实现储层识别.由于KPCA-SVM模型集成了核函数、主成分和支持向量分类机的优点,较好地解决非线性小样本的问题,能消除数据之间的噪音,降低维数,而又不缺失有效信息,达到准确快速预测的功能.将该模型应用到新场须二气藏新856井区储层预测中,预测结果验证了本模型的优越性,可作为致密储层预测的可选方法. 相似文献
14.
Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five-fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects. 相似文献
15.
Natan Micheletti Loris Foresti Sylvain Robert Michael Leuenberger Andrea Pedrazzini Michel Jaboyedoff Mikhail Kanevski 《Mathematical Geosciences》2014,46(1):33-57
This paper explores the use of adaptive support vector machines, random forests and AdaBoost for landslide susceptibility mapping in three separated regions of Canton Vaud, Switzerland, based on a set of geological, hydrological and morphological features. The feature selection properties of the three algorithms are studied to analyze the relevance of features in controlling the spatial distribution of landslides. The elimination of irrelevant features gives simpler, lower dimensional models while keeping the classification performance high. An object-based sampling procedure is considered to reduce the spatial autocorrelation of data and to estimate more reliably generalization skills when applying the model to predict the occurrence of new unknown landslides. The accuracy of the models, the relevance of features and the quality of landslide susceptibility maps were found to be high in the regions characterized by shallow landslides and low in the ones with deep-seated landslides. Despite providing similar skill, random forests and AdaBoost were found to be more efficient in performing feature selection than adaptive support vector machines. The results of this study reveal the strengths of the classification algorithms, but evidence: (1) the need for relying on more than one method for the identification of relevant variables; (2) the weakness of the adaptive scaling algorithm when used with landslide data; and (3) the lack of additional features which characterize the spatial distribution of deep-seated landslides. 相似文献
16.
Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management 总被引：1，自引：0，他引：1
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices. 相似文献
17.
Zhong-ping Sun Suhong Liu Fei Cao Yuanli Shi Chang-zuo Wang 《Arabian Journal of Geosciences》2017,10(22):495
Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68). 相似文献
18.
Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution
The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification. 相似文献
19.
基于聚类-二叉树支持向量机的砂土液化预测模型 总被引：3，自引：1，他引：2
建立在统计学习理论基础之上的支持向量机（SVM）,是一种基于结构风险最小的小样本机器学习方法。经典的支持向量机主要针对二分类问题,而工程实践中遇到的往往是多分类问题。根据影响砂土液化的主要因素,采用聚类分析中的类距离思想,建立了基于聚类-二叉树的多类支持向量机的砂土液化判别模型。该模型可以通过有限样本的学习,建立砂土液化与各影响因素之间的非线性关系。研究结果表明,基于聚类-二叉树支持向量机的层次结构合理,分类精度高,泛化性好,可对砂土液化等级进行较准确判别 相似文献