利用经验模态分解和主成分分析的SAR图像相干斑抑制

对SAR图像应用对数加性噪声模型,将经验模态分解(Empirical mode decomposition, EMD)与主成分分析(Principal Component Analysis, PCA)相结合,提出了一种基于PCA的EMD相干斑抑制算法。根据对数SAR图像中相干斑噪声的统计特性和高斯白噪声经EMD分解后的能量分布模型,近似估算SAR图像经EMD分解后各层内蕴模态函数中所含噪声的能量;将内蕴模态函数利用PCA进行分解,根据PCA对含噪信号的分解特性和内蕴模态函数中噪声能量所占的比例,选择合适的成分分量重构内蕴模态函数,以进一步去除噪声保留有用的细节信息。仿真实验结果表明,本文方法在有效抑制相干斑噪声的同时可以较好地保持边缘纹理细节的清晰。     

从MODIS资料提取土壤湿度信息的主成分分析方法

沿袭了遥感地物分类中K-L变换思想, 以归一化处理后的遥感数据, 结合地面土壤湿度观测数据, 应用主成分分析方法, 采用MODIS不同红外波段数据, 通过相关关系计算, 在监测结果中融合MODIS具有250 m分辨率的第2波段数据, 建立了青海省多维特征空间土壤湿度监测模型。模型的建立克服了热惯量法监测干旱需多时相遥感数据的缺陷, 经初步检验, 此模型可以在一定精度范围之内监测表层土壤湿度, 具有业务应用潜力。     

Model-based remote sensing algorithms for particulate organic carbon (POC) in the Northeastern Gulf of Mexico

Hydrographic data, including particulate organic carbon (POC) from the Northeastern Gulf of Mexico (NEGOM) study, were combined with remotely-sensed SeaWiFS data to estimate POC concentration using principal component analysis (PCA). The spectral radiance was extracted at each NEGOM station, digitized, and averaged. The mean value and spurious trends were removed from each spectrum. De-trended data included six wavelengths at 58 stations. The correlation between the weighting factors of the first six eigenvectors and POC concentration were applied using multiple linear regression. PCA algorithms based on the first three, four, and five modes accounted for 90, 95, and 98% of total variance and yielded significant correlations with POC with R ² = 0.89, 0.92, and 0.93. These full waveband approaches provided robust estimates of POC in various water types. Three different analyses (root mean square error, mean ratio and standard deviation) showed similar error estimates, and suggest that spectral variations in the modes defined by just the first four characteristic vectors are closely correlated with POC concentration, resulting in only negligible loss of spectral information from additional modes. The use of POC algorithms greatly increases the spatial and temporal resolution for interpreting POC cycling and can be extrapolated throughout and perhaps beyond the area of shipboard sampling.     

江西会昌岽背玄武岩地球化学和Sr-Nd同位素特征及其地质意义

江西会昌岽背地区晚中生代玄武岩的SiO2含量为49.06%～50.14%,K2O含量为1.26%～1.63%.其微量元素显示出大离子亲石元素富集,高场强元素亏损,尤以明显的Nb、Ta亏损为特征;REE总量为(135.5～146.8)×10-6,(La/Yb)N≈10,表现出LREE富集的稀土元素配分模式,且无明显的Eu异常;Sr-Nd同位素具有高(87Sr/86Sr)i(0.70678～0.70695)和低εNd(t)(-2.46～-2.18)的特征.据以上特征并结合区域地质背景,反映出岽背玄武岩的源区可能为EMⅡ型富集型大陆岩石圈地幔,其形成的构造环境为活动大陆边缘.较低的Nb/U比值(7～8)暗示该玄武质岩浆在形成过程中遭受了一定程度的大陆地壳混染或与俯冲过程有关的物质的加入.岽背玄武岩具有陆缘弧型地球化学特征,指示会昌地区在晚白垩世晚期(约85 Ma)处于活动大陆边缘的构造环境,该玄武岩的形成可能与燕山晚期古太平洋板块向欧亚大陆的俯冲消减作用关系密切.底侵作用可能为会昌地区燕山晚期成矿岩浆的生成提供了流体和热,有利于铜多金属矿的形成.     

北太平洋500hpa候高度场的主成分分析

对太平洋500hPa候高度场和距平场进行了主成分分析,得出反映500hPa候高度特征的典型场。指出候高度场的第1特征向量的空间型是季节变化的主要型,它可解释总方差的74.5％,候高度场的其余特征向量与距平场的特征向量是按顺序的对应相似。这些典型场可以很好地描述北太平洋的大气环流特征。距平场的第1特征向量空间型清楚地反映了与厄尔·尼诺现象有关的环流型。     

基于独立分量分析的遥感图像分类技术

遥感图像的自动分类方法一般基于图像的统计信息。多光谱遥感图像之间有着一定的相关性 ,对遥感图像的自动分类有不利影响。一般用主成分分析去除波段之间的相关性。独立分量分析能利用相对主成分分析更高的统计分量 ,不但可以获得去相关的效果 ,而且可以得到相互独立的结果波段图像。本文首先讨论了独立分量分析的基本原理。在此基础上 ,介绍FastICA算法 ,并对其进行改进 ,得到M FastICA算法 ,并将其应用到遥感图像的分类上。实验结果表明 ,M FastICA算法较FastICA算法收敛性大为改善 ,提高了独立分量分析在遥感图像的分类上的有效性     

主成分分析法在地区综合实力评价中的应用

地区综合实力反映一个地区社会经济系统的发展水平。主成分分析法能够在保证原始数据信息损失最小的情况下,以少数的综合变量取代原有的多维变量,使数据结构大为简化,并且客观地确定权数,避免了主观随意性．因而是地区综合实力评价的一种有效方法。通过主成分分析,可以全方位地了解各个地区社会经济系统的发展水平及其差距。     

石家庄市大气PM1、PM2.5和PM10中重金属元素分布特征及来源的对比研究

为了解元素(尤其是重金属元素)在不同粒径大气颗粒物中的分布规律、污染特征及来源,于2016年在石家庄市采集PM1、PM2.5和PM10样品。利用等离子体质谱仪(ICP-MS)测定了Al、Ti、V、Cr、Mn、Fe、Cu、Zn、As、Sb、Hg、Pb和Cd共13种元素的质量浓度,采用富集因子(EF)法分析各种元素在PM1、PM2.5和PM10中的分布特征,并通过主成分分析法讨论了这些元素的主要来源。富集因子分析显示出Al、Fe、Ti、Mn受人为因素影响较少(EF<10),其他元素则出现显著至极强的人为影响,尤其是Cd元素(EF>103),并发现颗粒物粒径较小时,富集因子较大,即人为因素影响更重。主成分分析表明：PM1中元素有工业冶炼及燃煤活动、机动车燃油排放、生活燃煤3个来源,PM2.5中元素有地壳源、电厂及居民生活燃煤、金属冶炼等工业活动、机动车尾气4个来源,PM10中元素有化石燃料燃烧和地壳源、与机动车相关的道路扬尘及工业尘、垃圾焚烧及机动车排放与磨损、燃煤活动4个来源。     

Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt,SW China

Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(i.e.magmatism,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during mineralization.Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups.Three steps of data analytical procedures are described in this paper; firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes; and finally,identifying ore-finding targets based on the anomaly components.This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting.It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.     

关于MapGIS数据文件转换中色库处理技术探讨

针对不同软件向MapGIS进行文件转换过程中遇到统一色库的问题,本文着重介绍了两种色库的转换方法,并编制了相应的程序,实例测试印证了该方法的正确性与可行性,从而解决了不同软件向MapGIS转换的颜色处理问题。