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

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.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Isotope hydrological study of mean transit time in the granitic Strengbach catchment (Vosges massif,France): application of the FlowPC model with modified input function

Measurements of ¹⁸O concentrations in precipitation, soil solution, spring and runoff are used to determine water transit time in the small granitic Strengbach catchment (0·8 km²; 883–1146 m above sea level) located in the Vosges Mountains of northeastern France. Water transit times were calculated by applying the exponential, exponential piston and dispersion models of the FlowPC program to isotopic input (rainfall) and output (spring and stream water) data sets during the period 1989–95. The input function of the model was modified compared with the former version of the model and estimated by a deterministic approach based on a simplified hydrological balance. The fit between observed and calculated output data showed marked improvements compared with results obtained using the initial version of the model. An exponential piston version of the model applied to spring water indicates a 38·5 month mean transit time, which suggests that the volume in the aquifer, expressed in water depth, is 2·4 m. A considerable thickness (>45 m) of fractured bedrock may be involved for such a volume of water to be stored in the aquifer. Copyright © 2005 John Wiley & Sons, Ltd.     

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

IntroductionThe radiance leaving the earth-atmosphere sys-tem which can be sensed by a satellite borne ra-diometer is the sum of radiation emission fromtheearth surface and each atmospheric level that aretransmittedtothe top of the atmosphere.The radia-tion emissionfromthe earthsurface andthe radianceof each atmospheric level can be separated fromtheradiance at the top the atmospheric level a satellitemeasured.Thus,the earth surface parameters willbe retrieved from the surface radiance after a…     

利用Lageos 1 SLR资料解算1990-2001年的地球定向参数

利用 12年的Lageos 1卫星激光测距资料 (1990 - 2 0 0 1)解算得到了地球定向参数 (EOP) ,将该序列的结果与同期的EOP(IERS)C0 4进行比较 ,其外符精度为 :极移XP— 0 .4 0mas,YP— 0 .4 2mas ,日长变化Dr— 0 .0 35ms。     

应用ATA的对数周期馈源方案于FAST的可能性探讨

介绍应用于美国凤凰计划的Allen望远镜阵(ATA)的对数周期馈源(LPA)概况，初步给出此种馈源的基本参数及性能，仿真估算了其驻波比和方向图，并结合正在进行中的我国500m孔径球面射电望远镜(FAST)计划，探讨了应用这种宽带馈源的可能性及其限制。     

A qualitative and quantitative evaluation of experimental model catchment evolution

Due to the geological time scales required for observation of catchment evolution, surrogates or analogues of field data are necessary to understand long‐term processes. To investigate long‐term catchment behaviour, two experimental model catchments that developed without rigid boundaries under controlled conditions are examined and a qualitative and quantitative analysis of their evolution is presented. Qualitatively, the experimental catchments have the visual appearance of field scale data. Observation demonstrates that changes in catchment shape and network form are conservative. Quantitative analysis suggests that the catchments reach an equilibrium form while a reduction in the channel network occurs. While the catchments are laboratory scale models, the results provide insights into field scale behaviour. Copyright © 2003 John Wiley & Sons, Ltd.     

THE SIMULATED STORAGE CAPACITY OF FLOOD AND WATERLOGGING IN THE TYPICAL AGRICULTURAL REGION IN JIANGHAN PLAIN

１ＩＮＴＲＯＤＵＣＴＩＯＮＪｉａｎｇｈａｎＰｌａｉｎｉｓｏｎｅｏｆｔｈｅｉｍｐｏｒｔａｎｔｂａｓｅｓｏｆＣｈｉｎａ′ｓａｇｒｉｃｕｌｔｕｒａｌｐｒｏｄｕｃｔｉｖｉｔｙｌｏｃａｔｅｄｉｎｔｈｅｍｉｄｄｌｅＣｈａｎｇｊｉａｎｇＲｉｖｅｒｖａｌｌｅｙｏｆｃｅｎｔｒａｌＣｈｉｎａ．ＴｈｅＪｉａｎｇｈａｎｐｌａｉｎａｐｐｒｏｘｉｍａｔｅｌｙ６．６×１０４ｋｍ２ｉｎａｒｅａａｎｄｏｖｅｒ３０ｍｉｌｌｉｏｎｉｎｐｏｐｕｌａｔｉｏｎｉｓｔｈｅｒｅｎｏｗｎｅｄ“ｃｏｕｎｔｒｙｏｆｆｉｓｈａｎｄｒｉｃｅ＂ｉｎ…     

重力插值方法研究

为了根据离散观测数据构制连续空间重力变化图像，分析和讨论了３种数值插值方法，计算结果表明多面函数方法插值精度最高。由于逐步回归分析筛选核函数中心点的计算繁琐，文中提出根据分形理论和Ｓｈａｎｎｏｎ取样定理来确定核函数中心点。对滇西试验场进行模拟试算，插值精度可达到４～５（１０－８ｍｓ－２）。     

Study on numerical simulation to water lowering in an open coal mine digging

In view of the situation of excavation of open coal mine for the underground water disaster,we should carry out simulation studies for the numerical value of the water lowering project and improve the accuracy and the level of the water lowering project.On the basis of the hydrological geological conditions of certain open mine digging,a more reasonable seepage numerical model was built according to MODFLOW.It was simulated in advance that the process of the confined water level descending with the time,and combining with the actual observations to test the correctness of the model.The calculation showed that the results coincided well with the results of actual measurement.Based on this,different water lowering numerical simulations were built for the open coal mine digging.It could be simulated and forecast that the changes of the groundwater level in drainage process within and outside the mine pit,and it was quantitatively assessed that the possible water lowering result of the opencast water drainage process,which provide an important basis for the actual water lowering project and the possible project disposal.