顾及太空风化效应的虹湾地区矿物含量反演

月表矿物含量反演是研究月球地质起源和演化的关键.太空风化作用普遍发生在月球表面,对矿物纯净光谱造成了不可忽视的影响,它弱化光谱吸收特征, 降低反射率,影响矿物含量遥感反演精度.基于Relab光谱库和Hapke辐射传输模型,将月表 4种矿物(单斜辉石、斜方辉石、斜长石、橄榄石)的二向性反射率转换成同向性的单次散射反照率,然后计算矿物的光学常数;再根据亚微观金属铁SMFe(submicroscopic metallic iron)的质量分数模拟6种不同程度太空风化效应,得到端元矿物的反射率光谱;最后基于上述方法,利用多端元线性分解方法和M3(moon mineralogy mapper,月球矿物绘图仪)高光谱数据反演不同风化程度下的矿物含量,得到月表虹湾地区辉石、斜长石、橄榄石3种矿物的含量分布.实验表明,利用多端元线性分解可以有效模拟太空风化效应对矿物光谱的影响,是研究太空风化效应影响下矿物识别及含量反演的一种行之有效的方法.      

基于实测高光谱数据空间特征的光谱混合模型

为提高高光谱混合像元分解精度, 利用地物多角度二向性反射平台和ASD FieldSpec3 Hi-Res便携式地物波谱仪, 设计等距离/等面积实验, 考虑探测距离远近对混合光谱的影响, 获取不同覆盖条件下叶片与方解石的混合光谱, 找出光谱数据空间特征变化规律, 提出等距离/等面积模型来消除空间位置对混合光谱的影响, 并将模拟混合光谱与实测混合光谱进行对比.通过实测数据分析, 混合反射率分布的权重系数随探测单位面积点与探头距离呈高斯变化规律; 与线性模型和线性改进模型进行光谱混合模拟结果相比, 应用权重系数高斯分布规律以等距离/等面积模型进行混合光谱模拟, 其模拟结果相似度平均增加了1.20%, 均方根误差平均降低了7.78%.等距离/等面积光谱混合模型考虑了光谱空间变化特征, 提高了光谱混合模拟的精度, 为进一步研究高光谱数据混合像元分解提供了新的方法.      

遥感影像像元组分分解模型及其应用

像元组分分解模型主要包括线性混合模型(LSMM)、模糊分类器模型、神经网络模型以及高斯混合判别分析模型(MDA)。以线性光谱混合模型为例给出了像元组分分解的过程,并分析了各种模型在不同领域的应用。像元组分分解还处于探索阶段,应根据不同的应用领域,不同的前提条件来选择和发展更为合适的模型,或用几种模型进行组合。端元的类型和数量的确定要力求准确,端元光谱值的获取要根据实际分析,研究更为适合的方法,使组分像元分解的精度得到进一步的提高。     

多源遥感数据在新疆卡拉麦里地区岩矿识别中的应用

遥感技术广泛应用于地质基础调查、矿产资源勘探、环境评估和地质灾害调查等方面。它已从多光谱发展到高光谱阶段,Landsat- 8是目前最具有代表性和最常用的多光谱数据,ASTER具有高的分辨率和多波段特征,资源一号02D(ZY1- 02D)卫星是我国2019年发射的高光谱业务卫星。为了更好地了解多源遥感数据在岩矿识别中的作用,在新疆东天山卡拉麦里地区进行了相关研究。结果表明：Landsat- 8 OLI的PCA变换结果清晰识别了研究区不同的岩性和地层;使用Landsat- 8 OLI、ASTER和ZY1- 02D高光谱数据,分别采取不同的图像端元提取方法,在进行光谱分析的基础上,利用光谱角填图（SAM）即可得到研究区的主要矿物分类图件。通过野外验证,应用GIS技术进行集成和分析,修正相关图件后,便得到了精准的矿物分类综合图。研究表明：多源遥感数据的集成在岩矿识别方面效果良好、前景巨大。     

Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.     

Identification and mapping of some soil types using field spectrometry and spectral mixture analyses: a case study of North Sinai, Egypt

This study examines linear spectral unmixing technique for mapping the surface soil types using field spectroscopy data as the reference spectra. The investigated area is located in North Sinai, Egypt. The study employed data from the Landsat 7 ETM+ satellite sensor with improved spatial and spectral resolution. Mixed remotely sensed image pixels may lead to inaccurate classification results in most conventional image classification algorithms. Spectral unmixing may solve this problem by resolving those into separate components. Four soil type end-members were identified with minimum noise fraction and pixel purity index analyses. The identified soil types are calcareous soils, dry sabkhas, wet sabkhas, and sand dunes. Soil end-member reference spectra were collected in the field using an ASD FieldSpec Pro spectrometer. Constrained sum-to-one and non-negativity linear spectral unmixing model was applied and the soil types map was produced. The results showed that linear spectral unmixing model can be a useful tool for mapping soil types from ETM+ images.     

Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space

Land desertification has been a worldwide environmental problem. Desertification monitoring and evaluation are very important content in desertification context. Scientific and accurate evaluation of desertification can provide scientific basis for decision making in mitigating desertification. Because of the advantage of large amount of information, short cycle and broad scope of data, less restrictions on the human and material resources and so on, remote sensing has become an important technology to monitor land desertification in the past 30 years. Desertification is the most typical and serious form of desertification in China, especially in the oasis zone distributed along inland rivers or in the lower reaches of inland rivers in northwestern China. Quantitative evaluation of the current desertification remote sensing methods used is mostly obtained through the vegetation index and vegetation cover, to gain information on the extent of desertification. As the arid and semiarid sparse vegetation cover, soil and soil moisture on the most common vegetation index have a greater effect. First, based on the spectral mixture analysis model, three kinds of endmember consisting of vegetation, water and bare soil were selected. The image dimensionality was reduced by the minimum noise fraction (MNF). The pixel purity index transformation was used to narrow the range of the endmember. On the scatter plot of MNF, three kinds of endmember were selected, and relative abundance distribution of each component was obtained by using linear spectral mixture model. Second, a spectral feature space composed of vegetation component and land surface albedo retrieved from Landsat TM Imagery was constructed to evaluate desertification present condition and degree quantificationally. Last, an empirical study was carried out taking the middle reaches of Heihe River as an example. Results indicated that this method makes full use of multi-dimensional remote sensing information, reflecting the desertification land cover, water, thermal environment and its changes, with a clear biophysical significance, and the index is simple, easy to obtain, high precision, and is conducive to quantitative analysis, monitoring and desertification assessment of desertification. It was rather ideal to assess desertification on the basis of Albedo-Vegetation feature space: correct prediction proportion of testing samples reached 90.3 %. This method was beneficial to the desertification quantitative analysis and monitoring with the characteristics of simple index, easy accessibility and high accuracy.     

An examination of the use of the logratio transformation for the testing of endmember hypotheses

Given a compositional dataset in the absence of any prior information on any mixing process which may have formed it, a complete analysis of mixtures determines three distinct types of estimates in order. These are: (i) the estimate of the number of endmembers or fixed source compositions, of which all the sample compositions of the dataset must be approximate mixtures; (ii) the estimated compositions for each of these chosen number of endmembers; and (iii) the estimated contributions of each of these endmember estimates to each sample. Traditionally, the estimate for the number of endmembers has been assessed either by mapping or by inspection of the coefficients of determination between the observed and estimated variables. Mapping entails the plotting on a map of the region from which the samples were taken, either the contours of the contributions of each endmember to each sample, or some other portrayal of the distribution of endmember abundances. Because it requires the complete analysis, assessment by this method is too elaborate except for final confirmation and display. Alternatively, choosing a number of endmembers, which result in suitability high coefficients of determination for all or most variables, may account for elements which are not part of the conjectured mixing process or, worse, may result in the identification of endmembers which may never in fact have existed. Such an error is similar to overspecifying a multiple regression model. So, the obvious starting point from which to assess the validity, or otherwise choice of endmember numbers, is to examine the matrix of residuals. The differences between the logratio-transformed observed and estimated data form an array of residual logratios. A linear combination of these may be formed for each sample, which, under a random perturbation assumption, should follow a univariate normal distribution. Whether or not this scalar is normal can be readily tested. It can also be examined graphically for such desirable qualities as symmetry when the test for normality may be too severe. This procedure is employed to assess the decompositions of the U.S.G.S. Mid-Pacific data and the Nazca Plate Surface sediments.This paper was presented at the 18th Geochautauqua, Newark, Delaware, 13–14 October 1989.     

An appraisal of an iterative construction of the endmembers controlling the composition of deep-sea manganese nodules from the Central Indian Ocean Basin

This paper describes an estimation of endmember compositions followed by the assessment of those results by log-ratio variance analysis. As an appraisal, it deals only with the first objective of an endmember analysis namely, to identify endmembers if they exist by estimating their compositions. Following the creation of the endmember estimates, the computation of an array of log-ratio variances was a key innovation in this type of study. Log-ratio variances revealed intrinsic linear associations between the dominant elements on each of the estimated endmember compositions, largely confirming the endmember analysis. The dataset under study contained the concentrations of 16 elements in 93 samples of deep-sea manganese nodules from the Central Indian Ocean Basin. Many previous analyses of these nodules were undertaken to assess the economic potential of the deposits. This study by contrast, quantified the inter-element associations that account for the nodule compositions. Four endmembers were identified. The elements loaded on each were: (1) Mn, Zn, Ni, Cu, Mn-rich, (2) Fe, Ti, P, Co, Fe-rich, (3) Si, Al, Na, K, clay minerals, (3) Mg, ultramafic material, possibly including Mn, Cr, V, Ca, Na. These latter elements were also detected by their log-ratio variances to be associated with Mg on the 4th endmember.     

Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification

The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction.     

Classification and Source Materials of Continental Crust Transformation Series Granitoids in South China

The granitoids of the continental crust transformation series in South China may be divided into threetypes: (1) synorogenic migmatic and magmatic type. (2) anorogenic continental crust anatexis type, and (3)syncollision type. Based on the results of Sr and Nd isotopic determinations, the source material compositionof the three types of granitoids is calculated with crust-mantle binary mixing simulation. The calculations indi-cate that the granitoids of the first type consist of 78.6-89.7% upper crust endmember materials and15.0-10.3% depleted mantle endmember materials, the granitoids of the second type are composed of 63.7%upper crust endmember materials and 36.3% depleted mantle endmember materials, and those of the third type100% upper crust endmember materials. Hence. the source material composition of the granitoids of all thethree types is dominated by upper crust endmembers.     

Copper Ore Identification using Spectral Similarity Measurement from Hyperion Image,Mapping of Porphyry Copper Mineralized Zone

This work illustrates the effectiveness of hyperspectal image spectroscopy and lab spectroscopy in identification techniques of minerals in alteration zone of ore body. The adopted procedure involves testing of Hyperion image spectra, their processing for noise, spectral matching and spectral similarity measurement with selected library spectra. Average weighted spectral similarity; visual and statistical matching techniques were used to select end-members from image spectra. Minimum noise fraction and pixel purity index technique were used to retrieve end-member spectra from hyperion image. Hyperspectral image like hyperion has the capability to deliver laboratory standard spectroscopic result. This paper illustrates the capability of hyperion image spectra in copper ore identification and mapping of chalcopyrite outcrop. A systematic approach has been made in this paper. This approach describes how image end member spectra and laboratory spectra can be co-related to fetch accurate spectral form of chalcopyrite ores. Thus, statistical and graphical comparison has been carried out between image derived end member spectra and laboratory spectra of chalcopyrite for better accuracy. The visual measurements is satisfactory, R = 0.973 for fine and 0.976 for medium grained chalcopyrite ore. Excellent statistical significance levels (90–97%) are found while comparing these spectra. There are many success stories of sub-pixel and N-dimensional feature space methods to separate the hydrothermal alteration zones from iron oxide mixed ore bodies. Thus, unmixing methods are very useful for mapping of most dominating mineral parts of a pixel from hyperspectral images which have coarse spatial resolution. Finally, mapping of mineralized zone has been achieved through sub-pixel based classifiers like spectral angle mapper (SAM), constrained energy minimization (CEM) and adaptive coherence estimator (ACE) techniques.     

高光谱数据处理及其在广西苗儿山地区的地质应用研究

本文以广西苗儿山地区机载成像细分红外高光谱数据为例,探讨了高光谱数据的特点及其处理方法。编制了一系列进行数据预处理的软件,进行了条带去除、波段间配准、航向压缩、镜向变换、辐射校正、正切校正、相对反射率转换等预处理,获得了高质量图像。在ＥＮＶＩ软件基础上建立了研究区野外实测光谱曲线库及分类子库,并对各子库进行了系统研究。同时,提出了正确的配准方案,对图像进行了精校正和镶嵌,取得了满意的镶嵌图像;在纯净像元提取、混合像元分解、匹配滤波、光谱特征拟合等方面进行了探索。最后,探讨了高光谱数据的地质应用潜力,尤其在硅化带识别方面取得了成功的效果,发现了一系列新的含铀硅化断裂带;预测了３片成矿有利地段,经验证,在其中两片地区找到了明显的铀偏高和矿化显示。     

Advances in Principal Balances for Compositional Data

Compositional data analysis requires selecting an orthonormal basis with which to work on coordinates. In most cases this selection is based on a data driven criterion. Principal component analysis provides bases that are, in general, functions of all the original parts, each with a different weight hindering their interpretation. For interpretative purposes, it would be better to have each basis component as a ratio or balance of the geometric means of two groups of parts, leaving irrelevant parts with a zero weight. This is the role of principal balances, defined as a sequence of orthonormal balances which successively maximize the explained variance in a data set. The new algorithm to compute principal balances requires an exhaustive search along all the possible sets of orthonormal balances. To reduce computational time, the sets of possible partitions for up to 15 parts are stored. Two other suboptimal, but feasible, algorithms are also introduced: (i) a new search for balances following a constrained principal component approach and (ii) the hierarchical cluster analysis of variables. The latter is a new approach based on the relation between the variation matrix and the Aitchison distance. The properties and performance of these three algorithms are illustrated using a typical data set of geochemical compositions and a simulation exercise.     

Endmember analysis of heavy-metal pollution in surficial sediments from the Gulf of Gdansk and the southern Baltic Sea off Poland

Thirty one bulk (<2 mm) samples of surficial sediments from the Gulf of Gdansk and the Southern Baltic Sea off Poland were analyzed for 20 elements and the resulting compositional dataset was subjected to endmember analysis. The compositions of 4 endmember estimates were constructed and the abundances of these endmembers were calculated for each sample. Copper, Zn and Ag appear on one endmember and Cd and Pb on another. This suggests that Cu, Zn and Ag are introduced into the Gulf of Gdansk principally from the Vistula River whereas Cd and Pb are introduced as a result of atmospheric and riverine transport of these elements. The dual source of Cd and Pb into the Gulf of Gdansk may explain the complex interelement relationships displayoed by these elements in the sediments from this region.     

基于资源一号02D卫星高光谱数据的甘肃头吊泉-南大滩地区蚀变矿物填图及金矿化带识别

高光谱遥感能够根据光谱指纹特征提取蚀变矿物,在地质领域应用成效十分显著.针对最新的资源一号02D卫星高光谱数据特点,首先进行了高光谱数据预处理,选取蚀变矿物光谱特征显著波段进行数据降维,并应用三次样条插值函数对短波红外数据进行光谱波段增值,将光谱采样间隔提高到了2 nm.在优选出矿物填图端元光谱后,应用改进的SAM填图...     

Endmember analysis of metalliferous sediments from the Galapagos Rift and East Pacific Rise between 2°N and 42°S

594 sediment samples from the Galapagos Rift System (GRS) and the crest of the East Pacific Rise (EPR) were chemically analyzed for 12 elements. Carbonate-free compositional datasets associated with each of 3 regions, the GRS, the EPR 2°N–4°S and the EPR 10°S–42°S, were separately subjected to endmember analysis. The compositions of endmember estimates were constructed for each dataset. These composition largely confirmed theidentities of endmembers that had previously been inferred from the varimax rotated loadings of a conventional factor analysis (of the correlation matrices) of the same 3 datasets.In view of the widely-reported unreliability of the correlation structure of compositional data, the confirmation by endmember analysis of the results of a factor analysis is itself quite remarkable. However, the particular advantage of endmember analysis is that the chemical compositions of extreme sources are estimated, and may readily be interpreted. The samples in the dataset can then be expressed as mixtures of these extreme sources. By contrast, the varimax rotated loadings of a factor analysis indicate only those elements that are associated together on a single factor which may or may not be an endmember, the composition of which nevertheless remains unknown.     

A method for the mathematical analysis of the cross-sectional geometry of tidal inlet channels

Analysis of channel cross sections is hindered by lack of parameters to describe the shape of the cross section. In the situation of a sample of cross sections taken across tidal inlets, if the cross section is expressed as an observation vector, principal-component analysis can be used to derive eigenvectors for the data set. By neglecting eigenvectors that explain little variance, mathematical representation of the original data set is simplified by transformation to the eigenvector space. For 408 cross sections each represented by a 60-component vector, three eigenvectors explain 97.5 percent of the total variance in the data set. The three-dimensional representation simplifies the task of analyzing cross-sectional shape. The physical form of the first three eigenvectors have considerable resemblance to classical types of variation noted in inlet-channel studies. The method is applicable directly to analysis of other fluvial and estuarine channels.     

高光谱遥感岩矿端元提取与分析方法研究

端元提取是高光谱遥感信息提取与分析的基础,也是解决高光谱图像混合像元分解的关键.本文针对研究区高光谱遥感数据特点,进行了辐射校正、最小噪声分离变换(MNF)及纯净像元指数(PPI)处理,在此基础上,应用二维散点图和三维散点图分别提取了端元波谱,并开展了端元属性的判别研究.岩矿端元的提取与分析为后续岩矿种类识别奠定了基础,直接影响最终成果的准确度.     

航空高光谱遥感矿物信息提取技术及其应用进展——以中国西部成矿带调查为例

为推进航空高光谱遥感矿物信息提取技术及其在地质工程化中的应用,2010—2015年,以我国西部成矿带为调查区,使用CASI/SASI/TASI航空高光谱数据,在进行矿物光谱特征分析、高光谱影像数据预处理、矿物信息提取、蚀变异常信息筛选及区域找矿预测基础上,编制了矿物种类分布图、单矿物丰度分布图和找矿预测图等高光谱地质调查系列专题图件; 建立了一套高光谱遥感矿物填图技术体系,解决了高光谱数据预处理与矿物提取等方面的技术问题,推进了地质填图向精细化和微观化方向发展。该研究为高光谱技术在地质工程化中的应用奠定了基础,丰富了地质填图的产品类型和内容,并服务于地质找矿等地质工作。