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.     

A numerical investigation of the destruction of peroxy radical by Cu ion catalysed reactions on atmospheric particles

To determine if Cu mediated reactions on atmospheric particles are important to HO₂ chemistry in the ambient atmosphere, Cu molalities were calculated from measured Cu aerosol particle concentrations, mass and number size distribution data from a site in central Sweden. A comparison of characteristic times indicates that at low relative humidities the reaction is limited by the mass transport of gas phase HO₂ to the particle surface and not by the chemical kinetics of the aqueous reaction. Comparison of half-lives for particle reactions and the gas phase destruction of HO₂ to form H₂O₂ indicate that heterogeneous reactions on aerosol particles may have important consequences on the chemistry of HO₂ and H₂O₂ in the troposphere.     

非线性洋面温度反演

本文从辐射方程出发，简述洋面温度与亮度温度之间的非线性关系，进行非线性洋面温度反演的方案设计，用正演算法 计算出非线性洋面温度反演公式的回归系数，对样进行检验。     

气溶胶的光学厚度与反射率比的处理方法

气溶胶光学厚度与气溶胶反射率比都是大气校正所需的重要大气参数，同时也是海洋水色卫星主要的数据产品，它们的测量精度将直接到卫星数据产品正演的精度和卫星数据产品的应用。文章在简述气溶胶光学厚度与气溶胶反射率比的基本测量原理和处理方法的基础上，结合多次试验数据结果进行简要的评价。     

Atmospheric input of trace elements to the western Pacific Ocean and the Kuroshio ocean area

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基于分块分形编码的多姿态、表情人脸图像检索

利用图像分形编码中定义域块和最小均方误差一一对应的特点，提出了1种基于分形编码的多姿态、表情的人脸图像检索方法。该方法将待检索图像分割为相同大小的值域块，然后将每一值域块按给定的定义域块进行分形编码，得到最小均方误差，计算该最小均方误差与图像库中最小均方误差的欧氏距离。将待检索图像所有值域块的欧氏距离求平均，此平均欧氏距离较小的几幅图像即为检索出的图像。实验证明该方法能够准确地检索出图像库中存储的同一人的不同姿态、表情的图像。     

离子色谱分析在黄土和粉尘研究中的应用

黄土中存在着大量的碱金属及碱土金属，以往的测试方法多为原子吸收和ICP—MS，离子色谱法在测试黄土样品的碱金属和碱土金属元素含量中应用不多。将黄土地质标准样品进行消解后用离子色谱进行测试，测试效果良好，重复测试的相对标准偏差小于5％，与推荐值的相对误差小于5％，准确度和精密度符舍相关的质量控制要求。本实验方法测试的离子含量都在仪器响应呈线性的最佳测试范围之内，标准曲线经回归后相关系数均达到0．9999。黄土样品的分析结果与粉尘气溶胶样品的离子分析结果对比表明，此方法对于探讨粉尘起源以及沙尘暴天气的研究具有重要意义。     

南极中山站大气气溶胶的化学组成及其来源的判别

从1998年3月7日至1999年11月23日历时21个月，在南极中山站连续采集89个海洋气溶胶样品，本文提供全部样品的13种化学元素Cu、Pb、Zn、Cd、Fe、Al、Mn、Cr、V、K、Na、Ca、Mg含量的实测值．研究表明中山站气溶胶化学成份的含量具有季节性变化的特征．通过相关分析、因子分析、富集因子等方法判别不同时间段中山站气溶胶化学成份的来源。     

Ghost fishing in European waters: Impacts and management responses

In this paper we review levels of net loss, what happens to the gear once it has been lost, and the resulting levels of ‘ghost catches’ made in passive net fisheries in the EU. We also consider ghost catches resulting from lost gear in other types of fisheries, and the extent to which the value of ghost catches has been quantified. We consider why fishing gear is lost, and profile common management responses. We present a cost benefit model to assess the relative cost effectiveness of different management measures, and suggest that gear retrieval programmes may provide less value for money than other management responses.     

基于BP人工神经网络的水体遥感测深方法研究

利用Landsat7ETM＋遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,并对长江口南港河段水深进行了反演.结果表明：具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5 m的水深值反演精度较高,而对大于10 m的水深值反演精度较低.