Mineral mapping in the Pyramid Lake basin: Hydrothermal alteration, chemical precipitates and geothermal energy potential

Geothermal resources exist on the Pyramid Lake Paiute Tribal Lands (PLPTL) in northwestern Nevada. We compiled numerous indicators of these resources into a geographic information system along with concurrent investigative results. This effort required acquisition and analysis of spaceborne multispectral and airborne hyperspectral remote sensing data for early-stage geothermal exploration. We identified minerals such as alunite, kaolinite, and montmorillonite through analysis of hyperspectral data indicating regions of hydrothermally altered rock associated with areas of geothermal potential. Tertiary volcanic and granitic rocks also contain these indicator minerals. Quaternary environments displayed gypsum-bearing evaporite crusts that we postulate were deposited by sulfate-rich thermal waters. Throughout the PLPTL, tufa towers and tufa shoreline deposits are extensively distributed as remnants of paleo-lake Lahontan. Based on measured spectra of calcium carbonate, we mapped tufa towers elucidating the strike direction of associated faults. Additionally, we correlated remotely-derived maps of shoreline tufa deposits with climate-related changes in lake level. Our mapping results helped guide detailed exploration efforts to areas with the most geothermal potential.     

矿物与岩石的可见-近红外光谱特性综述

矿物和岩石的系统光谱学研究是当今高光谱遥感发展的基础。在简要介绍了吸收光谱产生的电子过程和振动过程机理后,系统归纳了主要矿物类型和岩石类型的可见-近红外光谱特征。认为矿物的吸收光谱主要由二价、三价铁离子和过渡族元素的电子跃迁以及晶体场效应等电子过程产生;此外,还可以由水分子、Al-OH、Mg-OH、碳酸根的振动过程及卤化物的色心和硫化物的导带产生;岩石的吸收光谱都是杂质、包体、蚀变及替代成分产生的,主要为铁、水和羟基、碳酸根、硼酸根的光谱。最后指出,不同物理、化学环境下的矿物光谱特征变异研究在将来的高光谱遥感地质勘察中将变的更为重要。     

Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra

A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1·3-2·5 μm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels, and multi-spectral and hyperspectral analysis techniques were used for analysis. Colour images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multi-spectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniques developed for analysis of hyperspectral image data can be applied to field and laboratory spectra in a variety of disciplines, and raise the question of the use of hyperspectral scanners in the laboratory.     

Estimating sulphide ore grade in broken rock using visible/infrared hyperspectral reflectance spectra

The field of hyperspectral remote sensing has developed rapidly for widespread mineral mapping from airborne platforms. The purpose of the current study was to examine whether hyperspectral spectrometry (0.35-2.5 w m) can be used in an underground mining environment for mapping the grade of sulphide ore in rock faces, hand specimens and core logging. Naturally broken samples of barren and ore-bearing rocks were collected from mines in the Sudbury Basin, Ontario, and dry and wet reflectance were measured. The sulphide minerals exhibit a one-sided absorption band at short wavelengths known as a conductance band. The hydroxyl-bearing silicates exhibit a triple absorption feature near 2.3 w m. Two ratios, one describing the conductance band and one describing the hydroxyl band, can be used to separate high grade ores (>20-25% sulphides) from barren and lower grade rocks. The conductance band ratio can also be used to estimate the concentration of chalcopyrite alone, - 15% chalcopyrite, absolute. Errors are proportional to the concentration of pyrrhotite and pentlandite. Errors can be reduced if total sulphides are estimated by other means, which a parallel study indicates is possible using thermal reflectance wavelengths. The study indicates that there is a high potential to use hyperspectral tools to grade sulphide ores.     

A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

From geological and planetary exploration perspectives, automated sub-pixel classification of hyperspectral data is the most difficult task as it involves blind unmixing with library spectra of minerals. In this study, we demonstrate a procedure involving spectral transformation and linear unmixing to achieve the above task. For this purpose, infrared spectra of rocks from the spectral library, field, and remotely sensed hyperspectral image cube were used. Potential spectra of minerals for unmixing rock spectra were drawn from the library based on similarity of absorption features measured using Pearson correlation coefficient. Eight transformation techniques namely, first derivative, fast Fourier transform, discrete wavelet transform, Hilbert–Huang transform, crude low pass filter, S-transform, binary encoding, spectral effective peak matching, and two sparsity-based techniques (orthogonal matching pursuit, sparse unmixing via variable splitting, and augmented Lagrangian) were evaluated. Subsequently, minerals identified by above techniques were unmixed by linear mixture model (LMM) to decipher mineralogical composition and abundance. Results of LMM achieved using fully constrained least-square-estimation-based quadratic programming optimization approach were evaluated by conventional procedures such as X-ray diffraction and microscopy. In the case of image cube, endmembers derived using minimum noise fraction and pixel purity index were subjected to above procedure. It is evident that the discrete-wavelet-transformation-based approach produced excellent and meaningful results due to its flexibility in scaling the data and capability to handle noisy spectra. It is interesting to note that the adopted procedure could perform sub-pixel classification of image cube automatically and identify predominance of dolomite in limestone and sodium in alunite based on subtle differences in absorption positions.     

Application of CASI/SASI and fieldspec4 hyperspectral data in exploration of the Baiyanghe uranium deposit,Hebukesaier, Xinjiang,NW China

Hyperspectral technology can quickly identify hydrothermal altered minerals and can locate the possible hydrothermal/mineralization centres through the variations of the spectral characteristic parameters of some specific altered minerals. In this study, airborne sensors Compact Airborne Spectrographic Imager (CASI)/ Shortwave infrared Airborne Spectrographic Imager (SASI) and visible-shortwave infrared spectrometer FieldSpec4 hyperspectral data were used to study the alteration characteristics to locate the possible hydrothermal/mineralization centres for the Baiyanghe uranium deposit, Xinjiang, NW China. The results showed that the hydrothermal altered minerals mainly included white mica (it is a general term for aqueous silicoaluminate mineral group, and mainly includes muscovite and illite in the text) (Al-rich, Al-median, and Al-poor white mica), montmorillonite, kaolinite, chlorite, carbonate, and ferric iron (Fe₂O_3, and the ferric iron alteration usually presents red, mainly hematite in the text); and that the alteration had obvious zoning characteristics. The variations in the absorption-peak wavelength of white mica at 2200 nm, SWIR-IC (short-wavelength infrared illite crystallinity, a dimensionless quantity, and it is defined as the absorption-peak depth of illite at 2200 nm divided by the absorption-peak depth of illite at 1900 nm on a hull quotient spectrum) of white mica, and acid–base properties revealed that the possible hydrothermal/mineralization centres were located in the northern contact zone between the Yangzhuang rock body and the northern volcanic rocks of the Devonian and in the acidic alteration zone to north of the deposit about 3.0–4.0 km, where there appeared a certain number of uranium mineralization occurrences. These results pointed to the existence of multiple hydrothermal activities and could provide references for periphery uranium exploration and geological origin of this uranium area.     

光谱遥感岩矿识别基础与技术研究进展

遥感技术的发展与地物光谱特征的研究密不可分。主要从光谱遥感发展与地质应用的趋势出发,从光谱遥感岩矿识别基础与识别技术方法两方面阐述了光谱遥感的研究进展。对于遥感岩矿的识别基础,主要阐述物谱关联和物理模型研究的技术方法与进展以及其对遥感地质应用的促进与深化。在技术方法方面,主要从多光谱与成像光谱两个层次上,分析利用光谱特征进行岩石矿物识别的研究进展及其潜力与可行性。强调了岩石矿物光谱特征在遥感岩矿识别与地质成因信息提取中的重要性。     

高光谱遥感岩矿识别填图的技术流程与主要技术方法综述

自20世纪80年代以来,伴随着多种航空和航天成像光谱仪的研制成功和投入运行,一系列的图像光谱基本处理手段和实验室岩矿光谱分析技术方法,如最小噪声分量(MNF)变换-像元纯度指数(Pixel Purity Index,PPI)-N维可视化(N-Dimensional Visualization)、岩石类型系统光谱分类、多变量统计分析、矿物光谱特征确认的概率分析、MGM技术等相继发展起来,从而为形成成像光谱岩矿填图的完整技术流程打下了坚实的技术基础。分析了多种岩矿光谱分析的技术方法,同时指出,不同方法具有各自的优、缺点,针对不同应用目标需要不同的矿物识别与岩矿填图方案;混合方法的开发应用和从可见光到微波波段的融合应用在未来更为重要。     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

Surface hydrothermal alteration mapping at Vulcano Island using MIVIS data

Remotely sensed data (airborne hyperspectral Multispectral Infrared and Visible Imaging Spectroradiometer (MIVIS) data) were used to map active hydrothermal areas at Vulcano island (Sicily), related to a shallow magmatic chamber inducing hot fluid movements in the overlying rocks. A multispectral analysis was carried out using selected MIVIS band combinations and field spectra of selected altered surfaces. Results were checked by field survey, especially in the most active La Fossa crater area, where the zonation of a superficial high sulphidation hydrothermal system was reconstructed. The band selection used was able to clearly discriminate between fresh and hydrothermally altered rocks, especially at La Fossa crater; but differences between advanced argillic and the silicic alteration facies, as identified by field spectra, were not recognized, probably due to the occurrence of fumaroles. Despite the bias introduced by vegetation cover, illumination conditions and buildings, previously unrecognized sites of potential hydrothermal alteration and fumaroles were mapped in the northern sector of Vulcano island.     

In situ reflectance measurements and TM data of some sedimentary rocks with emphasis on white sandstone,southwestern Sinai,Egypt

This work presents the results of in situ reflectance measurements and Landsat-TM data analysis for some sedimentary rocks exposed in southwestern Sinai. Particular emphasis was given to white sandstone. In situ reflectance measurements were carried out in four selected sites (Musaba Salama, Abu Natash, El Dehisa and Abu Qafas areas) to define the distinctive surface reflectance patterns caused by the abundance of silicates, carbonates, clay minerals and iron oxides in white sandstone and other sedimentary rock types (clay, kaolin, limestone and dolomite). Moreover, in situ measured radiometric data were used to establish the theoretical basis for lithological discrimination.

Enhanced band ratios (2/3, 3/4, 4/5 and 5/7) and colour stretched ratio composite (2/3, 3/4 and 5/7) of the TM data 25 January 1984 of the Musaba Salama area were utilized to identify rocks rich in silicates and thereby to distinguish the white sandstone. The results demonstrated that the processed TM data can be used reliably in arid regions to distinguish white sandstone from other sandstone varieties as well as other rock types (limestone, dolomite, kaolin and shale).     

Generic visual analysis for multi- and hyperspectral image data

Multi- and hyperspectral imaging and data analysis has been investigated in the last decades in the context of various fields of application like remote sensing or microscopic spectroscopy. However, recent developments in sensor technology and a growing number of application areas require a more generic view on data analysis, that clearly expands the current, domain-specific approaches. In this context, we address the problem of interactive exploration of multi- and hyperspectral data, consisting of (semi-)automatic data analysis and scientific visualization in a comprehensive fashion. In this paper, we propose an approach that enables a generic interactive exploration and easy segmentation of multi- and hyperspectral data, based on characterizing spectra of an individual dataset, the so-called endmembers. Using the concepts of existing endmember extraction algorithms, we derive a visual analysis system, where the characteristic spectra initially identified serve as input to interactively tailor a problem-specific visual analysis by means of visual exploration. An optional outlier detection improves the robustness of the endmember detection and analysis. An adequate system feedback of the costly unmixing procedure for the spectral data with respect to the current set of endmembers is ensured by a novel technique for progressive unmixing and view update which is applied at user modification. The progressive unmixing is based on an efficient prediction scheme applied to previous unmixing results. We present a detailed evaluation of our system in terms of confocal Raman microscopy, common multispectral imaging and remote sensing.     

Evaluating hyperspectral imaging of wetland vegetation as a tool for detecting estuarine nutrient enrichment

Nutrient enrichment and eutrophication are major concerns in many estuarine and wetland ecosystems, and the need is urgent for fast, efficient, and synoptic ways to detect and monitor nutrients in wetlands and other coastal systems across multiple spatial and temporal scales. We integrated three approaches in a multi-disciplinary evaluation of the potential for using hyperspectral imaging as a tool to assess nutrient enrichment and vegetation responses in tidal wetlands. For hyperspectral imaging to be an effective tool, spectral signatures must vary in ways correlated with water nutrient content either directly, or indirectly via such proxies as vegetation responses to elevated nitrogen. Working in Elkhorn Slough, central California, where intensive farming practices generate considerable runoff of fertilizers and pesticides, we looked first for long- and short-term trends among temporally ephemeral point data for nutrients and other water quality characters collected monthly at 18 water sampling stations since 1988. Second, we assessed responses of the dominant wetland plant, Salicornia virginica (common pickleweed) to two fertilizer regimes in 0.25 m² experimental plots, and measured changes in tissue composition (C, H, N), biomass, and spectral responses at leaf and at canopy scales. Third, we used HyMap hyperspectral imagery (126 bands; 15–19 nm spectral resolution; 2.5 m spatial resolution) for a synoptic assessment of the entire wetland ecosystem of Elkhorn Slough. We mapped monospecific Salicornia patches (～ 56–500 m²) on the ground adjacent to the 18 regular water sampling sites, and then located these patches in the hyperspectral imagery to correlate long-term responses of larger patches to water nutrient regimes. These were used as standards for correlating plant canopy spectral responses with nitrogen variation described by the water sampling program. There were consistent positive relationships between nitrogen levels and plant responses in both the field experiment and the landscape analyses. Two spectral indices, the Photochemical Reflectance Index (PRI) and Derivative Chlorophyll Index (DCI), were correlated significantly with water nutrients. We conclude that hyperspectral imagery can be used to detect nutrient enrichment across three spatial and at least two temporal scales, and suggest that more quantitative information could be extracted with further research and a greater understanding of physiological and physical mechanisms linking water chemistry, plant properties and spectral imaging characteristics.     

Detection of pre-defined boundaries between hydrothermal alteration zones using rotation-variant template matching

A new method for the detection of pre-defined boundaries in single-band image data that uses a rotation-variant template matching (RTM) algorithm is presented. This algorithm matches a miniature image of a pre-defined boundary to image data at various orientations. The image pixels that match boundary criteria are reported in output imagery together with the rotation angle of the template. The method is applied to identify boundaries between hydrothermal alteration zones in processed airborne hyperspectral imagery, based on the presence of white mica minerals. Results show that boundaries identified with RTM are relatively free of noise and more coherent than those identified with, for instance, image slicing techniques. Identified boundaries can be used for image segmentation. The output of the RTM algorithm also provides information on the type of boundary, whether it is crisp or gradual. This information can be used to better characterize mineral variation in the alteration halo associated with fossil hydrothermal systems.     

Reflectance spectra of minerals and their discrimination using Thematic Mapper,IRS and SPOT multi-spectral data

Abstract

Minerals and rocks show varying spectral reflectances under different spectral ranges of the electromagnetic spectrum and such spectral responses vary because of colour, texture, crystal structure, specific gravity and other physical and optical properties. Hence in order to optimise the spectral ranges which may be used in the recognition of the minerals and the rocks, spectral reflectance measurements were carried out for 29 minerals under the visible and near-infrared ranges of the electromagnetic spectrum covered by the TM bands 1, 2, 3 and 4, IRS bands 1, 2, 3 and 4 and SPOT bands 1, 2 and 3. The analysis of the data identifies the optimum spectral bands for distinguishing the different minerals and the mineral aggregates/rocks     

基于反卷积高层特征的遥感地物图像分类

目前利用深度卷积神经网络提取图像底层特征后分类效果已比较优秀,但是对于数据量大、波段多、波段间相关性高的多光谱遥感图像并非完全适用。针对多光谱遥感地物分类中常常出现的Hughes现象,即当训练样本一定时,模型的预测能力随着维度的增加而减小,提出了一种结合高层特征空间和迁移学习网络的遥感地物图像分类算法,利用两层堆叠的反卷积网络来提取目标数据集的高层特征,利用VGG16模型的卷积层权重来构建迁移网络模型,将高层特征导入迁移网络中加强训练得到更加优越的训练模型,利用训练模型可对多光谱遥感数据集更加准确分类。实验结果表明,在Satellite、NWPU和UC Merced实验数据中,冰川、建筑群和海滩分类精度得到有效提高,达到92%左右,针对沙漠、岩石、水域等特殊环境遥感图像,总体分类精度提高5%左右。部分多光谱遥感数据的底层特征和中层特征在训练器中表现并不理想,波段的增多也会导致信息的冗余和数据处理复杂性的提高,反而高层特征在这部分数据中保留了地物信息的轮廓,能更好地适应分类器,得到更加优越的分类结果。     

Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations

Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. In particular, NASA is continuously gathering very high-dimensional imagery data from the surface of the Earth with hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) satellite platform. The development of efficient techniques for extracting scientific understanding from the massive amount of collected data is critical for space-based Earth science and planetary exploration. In particular, many hyperspectral imaging applications demand real time or near real-time performance. Examples include homeland security/defense, environmental modeling and assessment, wild-land fire tracking, biological threat detection, and monitoring of oil spills and other types of chemical contamination. Only a few parallel processing strategies for hyperspectral imagery are currently available, and most of them assume homogeneity in the underlying computing platform. In turn, heterogeneous networks of workstations (NOWs) have rapidly become a very promising computing solution which is expected to play a major role in the design of high-performance systems for many on-going and planned remote sensing missions. In order to address the need for cost-effective parallel solutions in this fast growing and emerging research area, this paper develops several highly innovative parallel algorithms for unsupervised information extraction and mining from hyperspectral image data sets, which have been specifically designed to be run in heterogeneous NOWs. The considered approaches fall into three highly representative categories: clustering, classification and spectral mixture analysis. Analytical and experimental results are presented in the context of realistic applications (based on hyperspectral data sets from the AVIRIS data repository) using several homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and the University of Maryland.     

Investigation of heavy-mineral deposits using multispectral satellite data

Mineral deposit mapping is essential for sustainable and eco-friendly exploitation of natural resources. The south Tamil Nadu coast of India is rich in minerals. Currently the beach sands are extracted for export entirely in raw form without any value addition. Due to unsustainable sand mining, there are negative environment impacts, which lead to various coastal hazards such as erosion, salinization and sea-water intrusion. In order to initiate the focus on mapping of mineral deposits along this area, standardized hyperspectral analysis has been carried out using Landsat satellite data and Environment for Visualising Images (ENVI) software. The selected endmembers are identified by comparing the spectral signatures with predefined spectral plots from the United States Geological Survey (USGS) spectral library. Finally the endmembers are mapped with ENVI's spectral angle mapper (SAM). The minerals which show significant variation in reflectance at different spectral bands can be effectively mapped by using multispectral data. Ground verifications performed to assess the accuracy of classification were mostly in agreement with the obtained results. This study has opened up new areas for inland heavy mineral exploitation and leads to eco-friendly exploitation of natural resources along the study area. It also illustrates the high potential of multispectral satellite data for exploration and mapping of mineral resources.     

Commodity cluster-based parallel processing of hyperspectral imagery

The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.     

End-to-end simulation model of rover-based hyperspectral remote-sensing systems: application to VNIS

The simulation of remote-sensing hyperspectral images has various applications such as the design of future hyperspectral imaging systems, understanding of the image formation process, development and validation of data processing algorithms, and optimization of the instrument imaging mode. For incomplete understanding of the lunar surface and the wide environmental differences between earth and moon observation, imaging systems for lunar observation cannot be tested in their exact working environments before launch. In these cases, simulation of lunar hyperspectral images can be used as a powerful tool to analyse the imaging process on the moon. The VIS–NIR imaging spectrometer (VNIS) aboard the Chang’e-3 (CE-3) lunar rover is used to perform in situ mineral detection on the lunar surface, but the rover-based VNIS has some additional effects from the rover itself (e.g. the shadow caused by the rover). In this paper, a rover-based radiative transfer model has been developed, and the simulation model is able to generate realistic VNIS-like data in an automatic way under a set of user-driven instrument and illumination parameters. A realistic surface reflectance cube, as the original input for the simulation model, is provided by the interference imaging spectrometer (IIM) data of Chang’e-1 (CE-1). Several hyperspectral simulations in different illumination and observation geometries have been conducted to analyse the effects of shadow, specular irradiance, and diffuse irradiance on the imaging data. For certain illumination geometries, the simulation model can forecast image quality in different observation geometries; the model can also determine the optimal observation azimuth ranges at different solar elevation angles. Moreover, the simulation model can be used to provide test images for the rover effect elimination algorithms. These applications of the model can facilitate understanding by analysing the rover-based hyperspectral remote-sensing process and eventually obtaining high-quality images of the lunar surface.