An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing     

《International Journal of Applied Earth Observation and Geoinformation》2020

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.  相似文献   

Linux 防火墙的研究与设计   总被引：4，自引：0，他引：4  

陈果  尹治本 《成都信息工程学院学报》2003,18(2):125-131

阐述了Linux2．4内核的防火墙Linux的原理，并分析了netfilter的设计与实现，叙述了Linux的动态地址转换，介绍了在Linux下基于netfilter架构如何开发自己的防火墙并给出具体实例。  相似文献   

The spatial implications of the functional proximity deriving from air passenger flows between European metropolitan urban regions     

Malcolm C. Burns  Josep Roca Cladera  Montserrat Moix Bergadà 《GeoJournal》2008,71(1):37-52

Until recently the traditional spatial configuration of the European geography was based upon the core-periphery model. The ‘pentagon’, broadly defined as lying between London, Paris, Milan, Munich and Hamburg, was seen as the core area characterised by having the highest concentration of economic development in the European Union (EU), with the remainder of the European territory viewed as peripheral, albeit to varying degrees. In a number of cases such peripheral areas equated with clear regional disparities. The elaboration of the European Spatial Development Perspective (ESDP) (CEC, European spatial development perspective, towards balanced and sustainable development in the territory of the European Union, 1999) challenged this core-periphery model. European spatial planning policies, aimed at encouraging social and economic, and with ever increasing importance, territorial cohesion, seek amongst other aspects to encourage the development of a balanced and polycentric urban system. This paper adopts a network analysis approach to the analysis of air passenger flows between some 28 principal European metropolitan urban regions. The evaluation of these flows contributes to an enhanced comprehension of the spatial dynamics of the European metropolitan territory which goes beyond that deriving from the more standard analyses of the individual components of the urban system. Several indicators are used, deriving from gravitational modelling techniques, to analyse the complexity of the air passenger flows. A multidimensional scaling (MDS) technique is introduced in order to interpret and visualise the resulting spatial configuration and positioning of the different metropolitan centres within the conceptual European ‘space of air passenger flows’, thereby contrasting with the more traditional map-based geographical image of Europe, based upon Cartesian coordinates.

Malcolm C. BurnsEmail:

  相似文献   

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models     

T. G. Sitharam  Pijush Samui  P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.  相似文献   

Neural network for wave forecasting among multi-stations     

Ching-Piao Tsai  Chang Lin  Jia-N Shen 《Ocean Engineering》2002,29(13):1685-1695

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data.  相似文献   

用Matlab中的Neural Network Toolbox仿真赤道东太平洋SST的预报模型   总被引：2，自引：0，他引：2  

张韧  蒋国荣  李妍 《海洋科学》2001,25(2):38-42

基于ＮＣＥＰ／ＮＣＡＲ再分析资料和ＣＯＡＤＳ海洋资料中的全球月平均海平面气压场、８５０hPa纬向风场及海洋温度场，利用Ｍatlab中的Ｎeural Network Toolbox仿真环境和ＢＰ模型改进算法比较准确地仿真和反演出了南方涛动指数、赤道纬向风指数和滞后的赤道东太平洋海温之间的动力结构和预报模型。该模型具有很好的拟合精度和可行的预报效果。可在一定时效内预测赤道东太平洋月平均海温的变化趋势。由于所建系统是具有直接因果关系的预报模型，因此不仅可直接用于预测，而且可有效避免类拟非线性微分方程组在积分过程中由于对初值敏感性而可能产生的对预报结果的不确定性。  相似文献   

复杂网络河的数值模拟   总被引：1，自引：0，他引：1  

拾兵  于诰方 《海洋湖沼通报》2002,(4):7-11

依据复杂网络河的特点及其内在联系，本文建立了其特征矩阵及由Newton-Raphson格式表述的隐式圣－维南差分方程组的系数矩阵，经输入边界条件及解方程组，可得到不同各支段上，下游端点上的水位和流量值。  相似文献   

关于城市交通地理信息系统模型设计的思考与改进   总被引：1，自引：0，他引：1  

王建强 《北京测绘》2008,(1):20-22

GIS—T模型是建立城市交通地理信息系统的主体,本文对现有GIS—T模型进行了简单的分析与评价,并且为了增加政府与公众做出决策的正确性,提出了路径畅通可靠度模型、城市交通综合指数模型、交通出行指数模型三个数学模型,以达到量化城市交通与完善GIS—T模型的目的。  相似文献   

电子地图符号构图规律的研究     

谢超  陈毓芬  柯希林  柳佳佳 《北京测绘》2008,(1):35-37

根据电子地图屏幕显示与阅读的特点,应用地图视觉感受理论及地图符号学理论对电子地图符号的构图规律进行研究,是有效地改进电子地图设计所必需的。本文从电子地图符号构图规律研究的必要性入手,根据对电子地图视觉变量的理解,应用地图视觉感受理论和地图符号学理论,对电子地形图符号体系进行研究,同时根据符号库建库的需要,将所有符号分解为最小图元,提出了点状、线状及面状符号的构图规律。  相似文献   

便携式电子地图设计中需解决的几个问题     

胡武强  杨华 《北京测绘》2008,(4)

软性显示器和投影技术超微型化的发展,使得电子地图有足够的空间满足便携式电子地图的设计需求,本文通过介绍几种便携式材质的特性,提出便携式电子地图设计中需解决的几个硬件技术问题。  相似文献