极化SAR影像弱散射地物统计分类

针对Wishart分类器对功率具有较强的依赖性, 不易区分极化SAR影像上水体、道路、裸土、阴影等弱后向散射地物的问题, 提出一种利用极化目标分解和假设检验的弱散射地物统计分类方法。即在H-α初始化的基础上, 使用似然比检验得出像元与每个类中心的相似性, 并将其作为像元与类中心的距离测度。根据第一类错误概率和统计量的概率分布, 将相似性很小的强散射点归为拒绝类, 减少对分类的影响;对不能显著拒绝的像元归入具有最小统计量的类别中。通过使用E-SAR L波段和Radarsat-2 C波段全极化数据进行实验, 结果表明本文方法有利于弱散射地物极化信息的利用, 能够实现水体、道路、裸露的土壤和阴影等的精确分类。     

Mapping dominant vegetation communities at Meili Snow Mountain,Yunnan Province,China using satellite imagery and plant community data

Mapping dominant vegetation communities is important work for vegetation scientists. It is very difficult to map dominant vegetation communities using multispectral remote sensing data only, especially in mountain areas. However plant community data contain useful information about the relationships between plant communities and their environment. In this paper, plant community data are linked with remote sensing to map vegetation communities. The Bayesian soft classifier was used to produce posterior probability images for each class. These images were used to calculate the prior probabilities. One hundred and eighty plant plots at Meili Snow Mountain, Yunnan Province, China were used to characterize the vegetation distribution for each class along altitude gradients. Then, the frequencies were used to modify the prior probabilities of each class. After stratification in a vegetation part and a non-vegetation part, a maximum-likelihood classification with equal prior probabilities was conducted, yielding an overall accuracy of 82.1% and a kappa accuracy of 0.797. Maximum-likelihood classification with modified prior probabilities in the vegetation part, conducted with a conventional maximum-likelihood classification for the non-vegetation part, yielded an overall accuracy of 87.7%, and a kappa accuracy of 0.861.     

Trivariate generalized extreme value distribution in flood frequency analysis

Abstract

The multivariate extension of the logistic model with generalized extreme value (GEV) marginals is applied to provide a regional at-site flood estimate. The maximum likelihood estimators of the parameters were obtained numerically by using a multivariable constrained optimization algorithm. The asymptotic results were checked by distribution sampling techniques in order to establish whether or not those results can be utilized for small samples. A region in northern Mexico with 21 gauging stations was selected to apply the model. Results were compared with those obtained by the most popular univariate distributions, the bivariate approach of the logistic model and three regional methods: station-year, index flood and L-moments. These show that there is a reduction in the standard error of fit when estimating the parameters of the marginal distribution with the trivariate distribution instead of its univariate and bivariate counterpart, and differences between at-site and regional at-site design events can be significant as return period increases.     

The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model

The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging, from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging, by adopting initial values from the first step on January 1 st , April 1st , July 1st , and October 1st , from 1982 to 2005. We generate 10 ensemble members for a particular start date (1st ) by choosing different atmospheric and land conditions around the hindcast start date (1st through 10th ). To estimate the predicted SST, two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions, and high anomaly correlation (>0.5) within 6 month lead time. Further, the ensemble approach is effective, in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system, as shown by anomaly correlation and equal likelihood. Nevertheless, the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04-0.14. At the same time, the ensemble system "equal likelihood" declines during spring. An ensemble mean helps give a correct prediction direction, departing from largely-deviated ensemble members.     

面向对象的森林植被图像识别分类方法

 森林植被信息提取是遥感影像分类中的难点,仅利用光谱信息难以提取森林植被的类型,本文以门头沟区森林植被占主要土地覆被类型为研究对象,选择HJ-1影像面向对象提取不同地物信息。由于研究区地形复杂,采用多尺度分割方法,对不同地物设置不同分割参数,实现不同地物分层提取。根据光谱、纹理及几何等特征选择合适的特征参数,构建隶属度函数,逐级提取研究区的土地覆被类型,并与传统的最大似然法进行对比。结果表明:面向对象的分类方法在门头沟区森林植被二级信息提取的精度为83%,与传统方法相比有了较大的提高。     

Methods for point source analysis in high energy neutrino telescopes

Neutrino telescopes are moving steadily toward the goal of detecting astrophysical neutrinos from the most powerful galactic and extragalactic sources. Here we describe analysis methods to search for high energy point-like neutrino sources using detectors deep in the ice or sea. We simulate an ideal cubic kilometer detector based on real world performance of existing detectors such as AMANDA, IceCube, and ANTARES. An unbinned likelihood ratio method is applied, making use of the point spread function and energy distribution of simulated neutrino signal events to separate them from the background of atmospheric neutrinos produced by cosmic ray showers. The unbinned point source analyses are shown to perform better than binned searches and, depending on the source spectral index, the use of energy information is shown to improve discovery potential by almost a factor of two.     

Application of EDA technology and μClinux operating system in the earthquake precursor instruments

Using Altera's Quartus Ⅱ, Nios Ⅱ IDE and Sopc Builder development tools, the proton precession magnetometer principle host hardware platform is designed in a cyclone Ⅱ series FPGA chip (EP2C35). The proton precession magnetometer principle host core circuit's single-chip system-logic design is achieved by building and configuring the Nios Ⅱ soft-core processor, developing the IO interface and sensor control circuits, programming some hardware units' VHDL code, for example the equal precision cymometer and the DPLL. Through researching the embedded operating system configuration technology and building the NIOS Ⅱ soft-core processor's μClinux cross-compile environment, the μClinux system is transplanted to the NIOS Ⅱ environment. Another important task is writing the device drivers' and user programs' code. Through these work, the design realize the host function and achieve the expected target.

     

Using Ancillary Information to Improve Hypocenter Estimation: Bayesian Single Event Location (BSEL)

We have developed and tested an algorithm, Bayesian Single Event Location (BSEL), for estimating the location of a seismic event. The main driver for our research is the inadequate representation of ancillary information in the hypocenter estimation procedure. The added benefit is that we have also addressed instability issues often encountered with historical NLR solvers (e.g., non-convergence or seismically infeasible results). BSEL differs from established nonlinear regression techniques by using a Bayesian prior probability density function (prior PDF) to incorporate ancillary physical basis constraints about event location. P-wave arrival times from seismic events are used in the development. Depth, a focus of this paper, may be modeled with a prior PDF (potentially skewed) that captures physical basis bounds from surface wave observations. This PDF is constructed from a Rayleigh wave depth excitation eigenfunction that is based on the observed minimum period from a spectrogram analysis and estimated near-source elastic parameters. For example, if the surface wave is an Rg phase, it potentially provides a strong constraint for depth, which has important implications for remote monitoring of nuclear explosions. The proposed Bayesian algorithm is illustrated with events that demonstrate its congruity with established hypocenter estimation methods and its application potential. The BSEL method is applied to three events: 1) A shallow Mw 4 earthquake that occurred near Bardwell, KY on June 6, 2003, 2) the Mw 5.6 earthquake of July 26, 2005 that occurred near Dillon, MT, and 3) a deep Mw 5.7 earthquake that occurred off the coast of Japan on April 22, 1980. A strong Rg was observed from the Bardwell, KY earthquake that places very strong constraints on depth and origin time. No Rg was observed for the Dillon, MT earthquake, but we used the minimum observed period of a Rayleigh wave (7 seconds) to reduce the depth and origin time uncertainty. Because the Japan event was deep, there is no observed surface wave energy. We utilize the prior generated from the Dillon, MT event to show that even in the case when a prior is inappropriately applied, high quality data will overcome its influence and result in a reasonable hypocenter estimate.     

一种基于对称结构优化的OSEM快速重建算法

由投影重建图像的EM重建算法中,有序子集最大期望值法能够对经典EM算法加速,提高收敛速度,受到广泛地关注.本文提出了一种基于对称性结构的OSEM快速重建算法,在更好地满足子集平衡的条件和优化有序子集的迭代顺序的同时,压缩了尺度窗口的计算.数据实验结果表明:当合适选取子集的数量时,S-OSEM算法有效地提高了重建速度和成像精度.     

Investigation of ‘equal displacement’ rule for bridges subjected to differential support motions

The ‘equal displacement’ rule is employed in seismic design practice to predict inelastic displacements from analyses of the corresponding linear elastic structural models. The accuracy and limitations of this rule have been investigated for ordinary structures but not for bridges subjected to spatially varying ground motions. The present study investigates this rule for moderate levels of inelastic behavior for four highway bridges in California accounting for the effects of spatial variability of the support motions due to incoherence, wave passage and differential site response. The bridge models vary significantly as to their fundamental periods and their overall configurations. Statistical analyses of pier‐drift responses are performed using as input simulated arrays of nonstationary ground motions in accordance with prescribed coherency models. It is found that the ‘equal displacement’ rule is fairly accurate for cases when the fundamental period of the bridge is longer than the transition period between the acceleration‐controlled and velocity‐controlled ranges of the response spectrum. Otherwise, the rule is non‐conservative for cases with large ductility factors and conservative for cases with small ductility factors. Wave passage and incoherence tend to reduce ratios of mean peak inelastic to elastic pier drifts, whereas incorporation of the differential site‐response effect by locating piers on softer soils tends to increase the same ratios. Mild or moderate positive correlation between these ratios and ductility demands is observed in most cases. Effects of spatial variability are more pronounced for longer and stiffer bridges. Copyright © 2013 John Wiley & Sons, Ltd.