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。     

华北石炭——二叠纪煤系粘土岩夹矸中铝的氢氧化物矿物研究

华北石炭——二叠纪煤系太原组和山西组不同煤层粘土岩夹矸中,广泛含有一水软铝石、一水硬铝石和三水铝石。大多数一水铝石矿物呈薄纹层状、似层状及层状聚集于富岩质及富有有机质粘土岩夹矸条带中,有的呈细脉状沿炭质条带及粘土岩夹矸层理的节理裂隙分布,并与玉髓、方解石、高岭石或迪开石和铵云母细脉共生。铝的氢氧化物矿物可以作为成岩古温度和成岩阶段划分的标志矿物。最后还讨论了该矿物的成因。     

金沙江下游白鹤滩水电站岩体结构的建造特征

拟建的白鹤滩水电站的坝基为峨眉山玄武岩。峨眉山玄武岩由火山熔岩类、火山碎屑熔岩类、火山碎屑岩类和沉积火山碎屑岩类所组成。火山熔岩又可划分为斜斑玄武岩、块状玄武岩和杏仁状玄武岩;火山碎屑岩包括集块岩、火山角砾岩以及凝灰岩;而沉积火山碎屑岩类则由沉火山角砾岩和沉凝灰岩所组成。不同类型岩石的结构构造、矿物成分和形成环境不同,导致它们的岩石力学性质和工程性能也不相同。块状玄武岩、斜斑玄武岩和沉积火山碎屑熔岩的抗压强度和抗风化能力都比较大,因而具有很好的工程地质稳定性;杏仁状玄武岩、火山碎屑熔岩的抗压强度稍低,但抗风化能力很好,因此也具有较好的工程地质稳定性;而火山碎屑岩包括火山角砾岩、凝灰岩的抗压强度和抗风化能力都很低,往往形成岩体中的软弱夹层,工程地质稳定性较差。     

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.     

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 %.     

Estimating surface ice velocity on Chhota Shigri glacier from satellite data using Particle Image Velocimetry (PIV) technique

Information about the surface ice velocity is one of the important parameters for Mass balance and Glacier dynamics. This study estimates the surface ice velocity of Chhota Shigri glacier using Landsat (TM/ETM+) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) temporal data-sets from a period of 2009 to 2016 and 2006 to 2007, respectively. A correlation based Particle Image Velocimetry (PIV) technique has been used for the estimation of surface ice velocity. This technique uses multiple window sizes in the same data-set. Four window sizes (low, medium, high, very high) are used for each image pair. Estimated results have been compared with the published data. The outcomes attained from the medium window size closely matches with the published results. The estimated mean surface ice velocities of medium window size are 24 and 28.5 myr^?1 for 2009/2010 and 2006/2007 images pair. Highest velocity is observed in middle part of the glacier while lowest in the accumulation zone of the glacier.