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.     

测井资料在碳酸盐岩储层评价中的应用

以塔河油田碳酸盐岩储集层基本特征为依据，利用常规和能谱测井资料信息比值法和重叠法等技术．致力于储集层缝、孔、洞测井响应特征敏感性评价研究，从中提取8项储层敏感性参数，并对其进行综合评判，确定出划分储层类别的标准，经过50口井的测试资料验证，采用多参数综合评判来划分储层类别是可行的，对储层的评价效果较好，使常规测井资料得到了更深入、广泛的应用。     

上饶市2000年6月22～23日连续暴雨过程浅析

对上饶市2000年6月22～23日连续暴雨的形势场以及卫星云图进行了分析,得出了一些有益的结论。     

Karst database development in Minnesota: design and data assembly

The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.     

从黄铁矿特征论380地区金鸡岭岩体外带铀矿床成矿机理

本文以铀矿区广泛发育的黄铁矿为研究对象，通过成因矿物学方法探讨金鸡岭岩体外带铀矿床的成矿机理；论证了矿质来源于岩体的围岩，与花岗岩关系不密切；岩体侵位所引起的围岩热变质作用，以及由此而产生的变质热液，是导致成矿物质重新活化、迁移和富集成矿的根本原因。     

基于北京一号卫星影像的建筑阴影测量建筑高度的应用研究

在分析遥感影像建筑物阴影与实际高度关系的基础上,阐述了依据影像阴影估算城市建筑物高度的原理和方法,设计了一种基于影像建筑物阴影特征快速提取高程信息的技术方法。在使用北京一号影像数据的试验中,70%的有阴影楼房测量误差在4米以内。     

宜春地区三类典型天气的风廓线雷达产品特征分析

选取江西省宜春市晴空、弱降水、强降水三类天气过程个例,结合天气形势、雷达回波、气象要素等资料,对比分析了风廓线雷达产品特征。结果表明：（1）在晴空天气背景下,风廓线雷达探测高度低,水平风速小,垂直风速正负值交替出现,大气折射率结构常数（Cn2）值最小。（2）在稳定性弱降水天气背景下,大气呈稳定状态,风廓线雷达探测高度随降水的产生而逐渐抬升,高低层有明显的风速切变,850 hPa赣南至赣东北有西南急流穿过,赣北有切变线存在,利于降水产生,垂直风速因降水影响出现朝向雷达正速度,Cn2值比晴空时大。（3）在具有产生强对流天气背景下,大气中对流强烈,风廓线雷达的水平风速增大,西南急流深厚且不断下沉,850-700 hPa有强烈的垂直切变,动力条件和水汽条件利于强降水产生,垂直风速表现为更大的朝向雷达正速度,Cn2值比弱降水时的大。     

慢特征分析法在气象上的应用进展

气候系统具有非平稳特征,根本原因在于其外强迫随时间发生改变,因此外部驱动力的分析对于理解气候系统的动力学特征至关重要,而如何有效提取系统外部驱动信息是一个亟待解决的前沿科学问题。最近几年,在生物神经学领域中应用的一种提取非平稳信号中外强迫信息的方法——慢特征分析法(Slow Feature Analysis,SFA),在气象领域中也得到了初步成功的尝试,结果显示出此方法对气候系统的外强迫信息分析及有关动力学机制的探究有较好的应用前景。本文主要介绍SFA方法的理论思想及实施步骤,并通过一个理想的非平稳时间序列检验其提取外强迫信息的能力,结果证明在衰减的Logistic模型中,可利用SFA算法提取出模型中的外强迫,且与真实外强迫的相关系数可达0.99;此外,还介绍将该方法应用于Arosa臭氧时间序列,分析其提取的外强迫信息的动力学特征;并介绍了在气候时间序列建模中引入外强迫因子的预测效果。     

Super-resolution enhancement of UAV images based on fractional calculus and POCS

Abstract

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images.     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.