Support vector machines and gradient boosting for graphical estimation of a slate deposit     

J.M.?MatíasEmail author  A.?Vaamonde  J.?Taboada  W.?González-Manteiga 《Stochastic Environmental Research and Risk Assessment (SERRA)》2004,18(5):309-323

Critical for an efficient and effective exploitation of a slate mine is to obtain information on its technical quality, in other words, on the exploitability potential of the deposit. We applied support vector machines (SVM) and LS-Boosting to the assessment of the technical quality of a new unexploited area of a mine, and compared the results to those obtained for kriging and neural networks. Firstly we analyzed the relationship between kriging and semi-parametric SVM in a regularization framework and explored the different alternatives for training these networks. Subsequently, in an attempt to combine both radial and projection structures, we formulated a boosting technique for radial basis function (RBF) networks defined over projections in the input space (RBFPP). The application of these techniques to our test drilling data demonstrated a similar level of performance for all the estimators examined, with the main difference occurring in the shape of the respective deposit reconstructions. Therefore, in choosing between the different techniques, an essential aspect will be their ability to reproduce the morphological characteristics of the true process. In this paper we also evaluate the benefits of using the estimated covariogram as the kernel of the SVMs and compare the sparsity of the different solutions. The results obtained show that the selection of a standard kernel that ignores the variability structure of the problem produces poorer results than when the estimated covariogram is used as the kernel.The research of J. Taboada was supported by the European Union, FEDER program, Project 1FD97–0091. The research of W. González-Manteiga was supported by Ministerio de Ciencia y Tecnología of the Spanish Government, Project BFM2002–03213. The authors wish to thank the associate editor and an anonymous referee for stimulating comments.  相似文献   

面向分类的高光谱遥感影像数据特性的研究   总被引：3，自引：3，他引：3  

辛宪会  赵泳  翟辉琴  叶秋果 《海洋测绘》2005,25(6):22-25

分析了高光谱遥感的特点、影像数据的应用现状,针对高光谱遥感影像处理、分类的现状及遇到的困难,对高光谱遥感影像数据的分类特性进行了深入的分析总结,对高光谱遥感影像分类的支持向量机方法进行了系统研究,并分析指出了支持向量机分类法的优越性,最后提出了需要进一步研究的问题.  相似文献   

VHSR图像基于分割对象分类器性能评价     

于海洋  甘甫平  武法东  党福星 《国土资源遥感》2008,(2):30-35

对比研究了平行六面体、最近邻分类法、最大似然法、神经网络等经典分类算法以及近年来新发展的支持向量机分类算法在基于分割对象的高分辨率遥感图像分类中的性能,详细分析了不同内积核函数对于支持向量机分类的影响。对两个试验区进行试验的结果表明,支持向量机分类算法分类精度得到明显改善,同时分类结果受参数、样本选择等影响较小,稳定性好。  相似文献   

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification     

《International Journal of Applied Earth Observation and Geoinformation》2016

Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector-machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification.  相似文献   

Age-based classification of arecanut crops: a case study of Channagiri,Karnataka, India     

B. E. Bhojaraja  Amba Shetty  M. K. Nagaraj  P. Manju 《国际地球制图》2016,31(9):995-1005

Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3–7, 8–15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer’s accuracy varied minimum of 12.5% for 3–7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut.  相似文献   

基于核主成分支持向量机的火成岩QAPF分类——以青海格尔木地区为例     

林楠  姜琦刚  陈永良  杨佳佳  崔瀚文 《地球学报》2014,35(4):487-494

利用核主成分(KPCA)较强的非线性特征提取能力对Hyperion高光谱数据进行降维及光谱特征提取,将特征信息作为支持向量机(SVM)建模样本的观测数据,建立KPCA-SVM回归模型,利用该模型进行研究区岩石氧化物百分含量反演。同时,依据国际地质科学联合会提出的QAPF火成岩分类方案对区内火成岩进行了岩性划分。研究结果表明:KPCA降维后的高光谱数据反演氧化物含量的效果良好;而基于QAPF模型的火成岩划分结果也十分理想,分类结果对已有地质图进行了有效的补充。KPCA-SVM理论模型为利用高光谱遥感数据进行岩性分类提供了一种快速可行的方法。  相似文献   

SVM和Elman神经网络在乌鲁木齐污染物预报中的应用研究     

蔡仁 《沙漠与绿洲气象(新疆气象)》2014,8(3):61-67

选用2012年11月1日-2013年1月31日的逐6 h的空气污染物（SO2、NO2、PM10）和实况气象要素（温度、湿度、能见度、风速和气压）资料,利用支持向量机和Elman神经网络方法建立空气污染物预报模型。结果表明,支持向量机和Elman神经网络方法都可以得到较为理想的预测结果,支持向量机在泛化能力方面具有显著优势,预测结果更加准确。  相似文献   

改进的OIF和SVM结合的高光谱遥感影像分类     

张磊  邵振峰 《测绘科学》2014,39(11):114-117,66

文章提出了一种结合改进的最佳指数法(OIF)和支持向量机(SVM)进行高光谱遥感影像分类新方法.利用本文提出的稳定系数进行波段初选择,根据相关系数选择波段组合生成新影像,并对新影像进行OIF计算,得到OIF值最大的波段组合为最佳波段组合;构建SVM分类器,对最佳波段组合分类;最后将分类结果与其他监督分类方法比较,并在相同核函数下与PCA和SVM结合的方法进行精度比较分析.实验结果表明,本文方法能够有效提取最佳波段组合,在SVM算法下获得较高分类精度.  相似文献   

Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.  相似文献   

基于支持向量机的遥感影像分类比较研究   总被引：2，自引：0，他引：2  

王小明;毛梦祺;张昌景;许勇 《东北测绘》2013,(4):17-20,23

支持向量机是建立在统计学习理论基础上的一种新的人工智能算法,较好地克服了传统分类方法中存在的小样本、非线性、过学习、高维数、局部极小点等问题,是一种极具潜力的遥感影像分类算法。本研究采用Landsat-5的TM影像,用支持向量分类法对影像进行分类,分析了支持向量机不同参数组合情况下的分类精度,并对支持向量分类法与传统分类方法进行了比较,发现支持向量分类算法具有参数选择范围宽,不要求对待分类区域地物光谱特征和影像分布特征具有先验知识,分类精度高等特点,对于在没有现场同步实测数据的区域进行精确的分类具有特别重要的价值。  相似文献