Une nouvelle méthode de cartographie de la région d’Oran (Algérie) à l’aide de la télédétection multispectrale

We have mapped the region of Oran, Algeria, using multispectral remote sensing with different resolutions. For the identification of objects on the ground using their spectral signatures, two methods were applied to images from SPOT, LANDSAT, IRS-1 C and ASTER. The first one is called Base Rule method (BR method) and is based on a set of rules that must be met at each pixel in the different bands reflectance calibrated and henceforth it is assigned to a given class. The construction of these rules is based on the spectral profiles of popular classes in the scene studied. The second one is called Spectral Angle Mapper method (SAM method) and is based on the direct calculation of the spectral angle between the target vector representing the spectral profile of the desired class and the pixel vector whose components are numbered accounts in the different bands of the calibrated image reflectance. This new method was performed using PCSATWIN software developed by our own laboratory LAAR. After collecting a library of spectral signatures with multiple libraries, a detailed study of the principles and physical processes that can influence the spectral signature has been conducted. The final goal is to establish the range of variation of a spectral profile of a well-defined class and therefore to get precise bases for spectral rules. From the results we have obtained, we find that the supervised classification of these pixels by BR method derived from spectral signatures reduces the uncertainty associated with identifying objects by enhancing significantly the percentage of correct classification with very distinct classes.     

ASTER spectral analysis for alteration minerals associated with gold mineralization

This study presents classifications of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery based on spectral analysis of alteration minerals associated with gold mineralization in Abo Marawat area which is located in the Central Eastern Desert of Egypt. Spectral analysis of continuum removed reference spectra of alteration minerals alunite, kaolinite, sericite, and calcite from USGS and JPL spectral libraries show shifts in position, shape, and strength most probably related to changes in sample purity and particle size of analyzed samples. Spectral Information Divergence (SID) classification method proved successful for mapping sericite, calcite, and clay minerals. Spectral Angle Mapper (SAM) classification identified only sericite and calcite alteration minerals. The identified alteration zones are coincidence with the field sampling and geological map of the study area. The microscopic examination of samples collected from the quartz veins and hydrothermally altered wall-rocks from near surface and subsurface at Abu Marawat gold mine shows sericitization, argillaceous, and carbonatization alteration zones. Gold occurs as very fine inclusions in pyrite, chalcopyrite, and sphalerite and also found filling the fractures between chalcopyrite grains. This study concludes that ASTER image classifications using reference spectra are a stable and reproducible technique for mapping gold related hydrothermal alteration zones in areas with no dominant vegetation cover.     

基于光谱匹配的热红外高光谱数据岩性分类研究

从岩石光谱出发,结合光谱谱带强度特征和光谱波形特征,针对机载热红外高光谱数据（TASI）,在以往算法基础上,提出一种改进的算法--光谱离散能级波形匹配法（SDEM）,并将其运用到岩性分类研究中。SDEM算法能识别岩石光谱间的微小差异,并在充分考虑光谱谱带强度和波形特征的同时,有效减弱数据噪声。与传统的岩性分类方法--高光谱角度制图法（SAM）相比,改进的算法能更精确地区分岩石相似光谱,识别易混淆岩性,对出现“异物同谱”现象的岩石也具有更好的区分能力。将SDEM、SAM方法应用于甘肃柳园地区TASI数据岩性分类研究中,可看出SDEM方法能识别出SAM未识别或识别错误的岩性。通过研究区野外查证,可知SDEM方法所得岩性分类结果更符合岩石实际分布情况。可见光谱离散能级波形匹配法具有较好的岩性分类效果,能更好地区分地物。     

Mapping the distribution of iron ore minerals and spatial correlation with environmental variables in hilltop mining areas

The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE?≤?0.51 MRSFF RMSE?≤?0.48), Goethite (SFF RMSE?≤?0.047 MRSFF RMSE?≤?0.438) and Desert (SFF RMSE?≤?0.63 and MRSFF RMSE?≤?0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.     

Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping

Hyperspectral images have wide applications in the fields of geology, mineral exploration, agriculture, forestry and environmental studies etc. due to their narrow band width with numerous channels. However, these images commonly suffer from atmospheric effects, thereby limiting their use. In such a situation, atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra of different surface materials/objects. In the present study, two very advance atmospheric approaches i.e. QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery. The spectra of vegetation, man-made structure and different minerals from the Gadag area of Karnataka, were extracted from the raw image and also from the QUAC and FLAASH corrected images. These spectra were compared among themselves and also with the existing USGS and JHU spectral library. FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption. These absorption curves in any spectra play an important role in identification of the compositions. Therefore, the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition. FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals. Therefore, this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals.     

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.     

Mediterranean forest mapping using hyper-spectral satellite imagery

Mediterranean forests are characterized by spatiotemporal heterogeneity that is associated with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote sensing can be an effective tool for characterizing and monitoring forest vegetation distribution within these fragmented Mediterranean landscapes. The heterogeneity of Mediterranean vegetation, however, often exceeds the resolution typical of most satellite sensors. Hyper-spectral remote sensing technology demonstrates the capacity for accurate vegetation identification. The objective of this research is to determine to what extent forest types can be discriminated using different image analysis techniques and spectral band combinations of Hyperion satellite imagery. This research mapped forest types using a pixel-based Spectral Angle Mapper (SAM), nearest neighbour and membership function classifiers of the object-oriented classification. Hyperion classification was done after reducing Hyperion data using nine selected band combinations. Results indicate that the selection of band combination while reducing the Hyperion dataset improves classification results for both the overall and the individual forest type accuracy, in particular for the selected optimum Hyperion band combination. One shortcoming is that the performance of the best selected band combination was superior in terms of both overall and individual forest type accuracy when applying the membership classifier of the object-oriented method compared to SAM and nearest neighbour classifiers. However, all techniques seemed to suffer from a number of problems, such as spectral similarity among forest types, overall low energy response of the Hyperion sensor, Hyperion medium spatial resolution and spatiotemporal and spectral heterogeneity of the Mediterranean ecosystem at multiple scales.     

Selection of Less Biased Threshold Angles for SAM Classification Using the Real Value–Area Fractal Technique

The accuracy of classification of the Spectral Angle Mapping (SAM) is warranted by choosing the appropriate threshold angles, which are normally defined by the user. Trial‐and‐error and statistical methods are commonly applied to determine threshold angles. In this paper, we discuss a real value–area (RV–A) technique based on the established concentration–area (C–A) fractal model to determine less biased threshold angles for SAM classification of multispectral images. Short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used over and around the Sar Cheshmeh porphyry Cu deposit and Seridune porphyry Cu prospect. Reference spectra from the known hydrothermal alteration zones in each study area were chosen for producing respective rule images. Segmentation of each rule image resulted in a RV–A curve. Hydrothermal alteration mapping based on threshold values of each RV–A curve showed that the first break in each curve is practical for selection of optimum threshold angles. The hydrothermal alteration maps of the study areas were evaluated by field and laboratory studies including X–ray diffraction analysis, spectral analysis, and thin section study of rock samples. The accuracy of the SAM classification was evaluated by using an error matrix. Overall accuracies of 80.62% and 75.45% were acquired in the Sar Cheshmeh and Seridune areas, respectively. We also used different threshold angles obtained by some statistical techniques to evaluate the efficiency of the proposed RV–A technique. Threshold angles provided by statistical techniques could not enhance the hydrothermal alteration zones around the known deposits, as good as threshold angles obtained by the RV–A technique. Since no arbitrary parameter is defined by the user in the application of the RV‐A technique, its application prevents introduction of human bias to the selection of optimum threshold angle for SAM classification.     

基于ASTER数据的蛇绿岩组分识别：以德尔尼矿区为例

本文运用高级星载热辐射和反射探测器(ASTER)反射率数据对青海德尔尼蛇绿岩的主要岩石组成和蚀变矿物进行探测。以现有的标准光谱库数据作为参照,采用光谱角制图法来提取感兴趣的岩性和矿物信息,并通过与现有地质图对比,验证结果的精度。实验结果表明,运用ASTER数据和标准的光谱库数据,可较有效地识别蛇绿岩的主要岩性和相关矿物成分,但不同岩性的识别精度不同。     

A new method of testing of the Earth’s free oscillations on the basis of geomagnetic variation analysis

The Earth’s free oscillations are recorded for the first time in variations of the geomagnetic field measured at the Earth’s surface. The Earth’s free oscillations in the frequency range from 0.3 to 4 mHz manifest in the spectra of geomagnetic variations in the form of clearly expressed quasi-harmonic peaks. It is shown that the spectral amplitudes of the main modes of the Earth’s free oscillations are not constant and change with a periodicity corresponding to a lunar (sideral) month. The data obtained indicate the influence of oscillations in the internal geospheres on variations in the terrestrial magnetic field. The results provide new opportunities to study the Earth’s free oscillations and to specify their multiplet components. In addition, they also have certain implications for further research into the internal structure of the Earth and geodynamic processes in internal geospheres, on the basis of magnetometric data.     

地球固体内核平动振荡的研究和检测

系统介绍了有关Slichter模的理论模拟及其超导重力观测检测的进展。地球固态内核的平动振荡是地球的基本简正模之一,又称Slichter模,以重力作为主要恢复力,其本征周期大约为几个小时。从理论模拟结果看,Slichter模的本征周期对于ICB密度差最为敏感,而ICB附近外核流体的粘滞性、内外核之间的过渡层以及Lorentz力等因素对周期的影响很小,Slichter模的研究和检测为了解地球中心附近的密度结构提供重要的信息。     

Application of Spectral Angle Mapper Classification to Discriminate Hydrothermal Alteration in Southwest Birjand,Iran, Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Image Processing

The purpose of this study is to evaluate the Spectral Angle Mapper (SAM) classification method for determining the optimum threshold (maximum spectral angle) to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits: Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

A multidimensional analysis and the chronologic structure of the Earth’s geodynamic activity

An attempt has been undertaken to examine time series of volcanic and seismic events in a multidimensional reference system related to the parameters of the Earth’s orbital motion. Volcanic eruptions and strong (M > 5) earthquakes (a sample from the USGS/NEIC seismological database: Significant Worldwide Earthquakes) [18] were analyzed within the fields of the JPL Planetary and Lunar Ephemerides, (DE-406) astronomical indicators [19]: the Earth-Moon distance, Earth-Sun distance, ecliptic latitude of the Moon, and the differences between the geocentric longitudes of the Moon and Sun, Venus and Sun, and Mars and Sun. Distribution spectra were obtained and normalization was performed taking the nonuniform motion of celestial bodies into consideration, and the values of multidimensional diurnal probability were calculated. As a result, the statistically reliable drift in the distribution of geoevents was calculated relative to the duration of the intervals of multidimensional diurnal probability, which indicates distribution regions where more geoevents can take place during shorter intervals (and vice versa). Linear relationships between the multidimensional diurnal probability and diurnal probability of geoevents were found. All these results and the astronomic ephemerides were used as a base for computing the probabilities of volcanic and seismic activity of the Earth for the period of 2005–2007. The spatial structure of volcanic and seismic processes was examined, which allowed the revelation of probabilistic parameters of the spatiotemporal structure of Earth’s geodynamic activity and outlining an approximate algorithm for its monitoring.     

基于ASTER的雅鲁藏布江缝合带泽当-罗布莎蛇绿岩套分析和组分识别

本文通过对西藏雅鲁藏布江缝合带泽当-罗布莎地区蛇绿岩套的主要岩石组成和蚀变矿物的标准波谱吸收特征分析,比较了标准光谱库的相应岩性光谱吸收特征和ASTER数据波段特征之间的关系,采用连续统去除、比值法和光谱角制图法对ASTER影像数据进行处理及相关岩性和矿物提取。结果表明,蛇绿岩组分岩性中亚铁离子和Fe-OH,Mg-OH的可见-短波红外吸收特征显著,而且有一个宽波长范围的Si-O热红外光谱特征,基于这些光谱特征采用ASTER数据和比值法与光谱角制图法可有效地识别蛇绿岩的主要岩性和相关矿物成分及其空间分布,结果与地质资料基本吻合。     

Laboratory technique for quantitative thermal emissivity measurements of geological samples

Thermal infrared spectroscopy is a powerful technique for the compositional analysis of geological materials. The spectral feature in the mid-IR region is diagnostic of the mineralogy and spectral signatures of mixtures of minerals that add linearly, and therefore, can be used as an important tool to determine the mineralogy of rocks in the laboratory and remotely for planetary exploration. The greatest challenge in the emission measurement lies in the measurement of the weak thermal photons emitted from geological materials in a laboratory setup, and accurately records the temperature of the rock sample. The present work pertains to the details of a new Thermal Emission Spectrometer (TES) laboratory that has been developed under the ISRO Planetary Science and Exploration (PLANEX) programme, for emission related mineralogical investigations of planetary surfaces. The focus of the paper is on the acquisition and calibration technique for obtaining emissivity, and the deconvolution procedure to obtain the modal abundances of the thermal emission spectra in the range of 6–25 μm using Fourier Transform Infrared (FTIR) spectroscopy. The basic technique is adopted from the work of Ruff et al (1997). This laboratory at the Department of Earth Sciences, IIT-Bombay is currently developing pure end mineral library of mineral particulates (<65 μm), and adding new end members to the existing ASU spectral library. The paper argues the need for considering Lunar Orbiter Thermal Emission Spectrometer (LOTES) for future Indian Moon mission programme (Chandrayan-II) to determine evidences of varied lithologies on the lunar surface.     

基于激光诱导击穿光谱技术的岩石表面指纹图谱分析及分类方法

岩石岩性识别在油气田探测开发、研究地球成因及演化发展、地质灾害分析预测等众多方面起着不可替代的导向作用,因此岩石的识别分类对于地质勘探分析来说至关重要。为了提高岩石的分类准确率,提出了一种基于激光诱导击穿光谱技术(LIBS)的岩石表面指纹图谱分析及分类方法。通过LIBS对岩石表面不同位置进行激发,获取原始光谱数据。对收集到的光谱数据进行去除异常点、归一化等预处理操作,根据岩石矿物成分确定五种含量差异较大元素(硅、铝、钾、钠、镁)的特征谱线并得到元素指纹图谱。然后选择支持向量机(SVM)作为分类器进行分类,分别建立利用光谱均值的分类模型和多维指纹图谱融合的分类模型,并对两种分类结果进行比较。利用光谱均值的分类模型准确率为59.4%,多维指纹图谱融合的模型分类准确率为96.5%。实验结果表明,元素指纹图谱展示了岩石表面元素分布,可以充分利用不同种类岩石本身的不均匀性结构信息,极大地提高了岩石的分类准确率。     

高分五号航天高光谱遥感技术在甘肃龙首山铀矿找矿中的应用

为深入研究和探讨高分五号(GF-5)航天高光谱遥感技术在铀矿地质找矿中的应用效果和潜力,基于龙首山成矿带航天高光谱数据,开展高光谱数据处理和蚀变信息提取工作,创新实现了GF-5高光谱波段修复,通过构建标准光谱库和诊断光谱,运用MNF算法、PPI算法,结合SAM光谱角填图技术,完成蚀变矿物端元提取和光谱匹配,实现研究区钠长石、方解石、石英、绿泥石、赤铁矿和高岭土蚀变矿物的提取,综合区域铀矿成矿地质背景,通过开展地面波谱测量和野外调查,在验证蚀变准确度的基础上,剖析航天高光谱蚀变信息和成矿规律,构建了区域找矿定位模型,圈定找矿预测区3处,取得了较好的找矿效果,为国产GF-5高光谱遥感在地质找矿中的应用提供了参考。     

ASD FieldSpec 3地物光谱数据在水磨—大河地区岩性分类中的应用

以四川省旺苍县水磨—大河地区为研究区, 利用美国ASD FieldSpec 3便携式地物光谱仪野外实测岩矿波谱数据和ASTER遥感影像数据, 基于GIS平台, 根据野外实测的岩石光谱曲线和USGS光谱库的典型岩石光谱曲线提取端元波谱, 对区域影像像元的光谱曲线进行匹配, 采用ENVI4.4软件自动信息提取与人机交互解译相结合的方式, 进行岩性分类, 可以有效地划分区内岩性界线, 满足填图需求, 对辅助该区区域基础地质调查、矿产普查等具有重要的应用价值。      

基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。