Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

山西北部至晋冀蒙交界地区几次中强地震前4.0级地震活动的研究

对山西北部至晋冀蒙交界地区中强地震前的 4 .0级地震进行了研究 ,得出其特征为异常增强—异常平静—信号地震的结论 ,认为该区某些 4 .0级地震活动似可作为某些中强地震的前兆地震而进行进一步的研究。     

基于树结构的数字遥感图像存储方法与应用

数字遥感图像一般是采用栅格结构来存储。图像中地物的结构特征是隐含其中的。该文从图像识别角度，重点研究了基于树结构的数字遥感图像存储方法。这种方法与遥感中常用的四又树等存储方法不同，它注重图像中地物本身的结构特征。通过树结构使像元按地物目标形成一个个的组合体。该文以机场跑道识别为例，介绍了基于树结构的数字遥感图像存储方法的应用以及将图像变为树的算法。     

关于地理信息系统学科的理论基础与体系框架

地理信息系统已成为一门独立的学科，关于它的理论基础与体系框架应该是一个值得研究的问题。从地理信息系统的本质看，它是传输地理环境信息的现代化工具，“空间认知”与“地理系统”作为地理信息系统的认识论和方法论，是地理信息系统的理论基础；地理信息系统理论、技术与应用构成了它的体系框架。     

新兴边缘学科——GPS气象学

阐述了GPS气象学的基本原理和GPS气象学的广阔应用领域，并介绍了GPS气象学的分类。     

我国月降水和气温网格点资料的处理和分析

用逐步搜索逼近法的客观分析技术计算出１９５１－１９９２年中国月降水量和月平均气温的格点资料，并利用图象识别原理对格点值和站点观测值进行了比较。     

A New Nonparametric Discriminant Analysis Algorithm Accounting for Bounded Data Errors

In a statistical pattern recognition context, discriminant analysis is designed to classify, when possible, objects into predefined categories. Because this method requires precise input data, uncertainties cannot be propagated in the classifying process. In real case studies, this could lead to drastic misinterpretations of objects. A new nonparametric algorithm based on interval arithmetic has thus been developed to propagate interval-form data. They consist in calculating interval conditional probability density functions and interval posterior probabilities. Objects are eventually assigned to a subset of classes, consistent with the data and their uncertainties. The classifying model is thus less precise, but more realistic than the standard one, which we prove on a real case study.     

风廓线雷达估算大气返回信号功率方法研究

基于风廓线雷达大气返回信号功率谱中噪声电平的估算方法,统计分析了北京延庆对流层风廓线雷达（CFL-08）2006年10～12月的探测数据。对该频段风廓线雷达环境噪声的空间和时间变化进行了分析,观测期间环境噪声在5km以下随高度递减,10月的平均环境噪声大于11月、12月的平均分布。给出目前风廓线雷达用信噪比估算大气返回信号功率的两种方法,并对两种方法进行了环境噪声的剔除,经过修正后的大气返回信号功率输出结果趋于一致。     

基于结构模式的道路网节点匹配方法

道路网数据中微观结构的识别对于多尺度路网建模、步行导航等至关重要。复杂道路交叉口是重要的道路微观结构之一,针对目前道路复杂交叉口基于几何形状描述与图形匹配识别方法存在的不足,从复杂交叉口识别与化简的角度出发,提出了一种利用路段分类进行复杂道路交叉口识别与化简的方法。该方法首先通过点密度聚类的方法对道路交叉口进行定位,然后利用路段的规模、形状和属性等特征构建特征空间,将交叉口的识别作为一种区分主干路段与辅助路段的两类分类问题,利用支持向量机的方法对交叉口区域内的路段进行分类,从而完成交叉口的识别与化简。利用开放街道地图（Open Street Map）数据进行实验,结果表明,该方法能够有效地识别道路交叉口。