Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071728
V. Lever, P. Foucher, X. Briottet, D. Dubucq, R. Oltra-Carrió, L. Poutier, V. Achard, P. Déliot
Soil-hydrocarbon mixtures give complex spectral responses. This has prohibited any physical modelling until now. Spectral analysis and quantification of contamination rate has been performed by regression models, calibrated on spectral databases. Only lab or field databases have been used. This study proposes an innovative joint lab-field-airborne spectral database in the reflective domain (0.4–2.5/xm) to assess the performance of regression models on airborne images of soil-hydrocarbon mixtures. Sample preparation and spectral measurements are described. Implied instruments are an ASD FieldSpec Pro 2 spectrometer and the HySpex hyperspectral camera. Accordance between ground truth and airborne data is shown. Several raw outdoor spectra are displayed.
土壤-碳氢化合物混合物具有复杂的光谱响应。到目前为止，这已经禁止了任何物理模型。光谱分析和污染率的量化是通过回归模型进行的，并在光谱数据库上进行校准。仅使用了实验室或现场数据库。本研究提出了一个创新的反射域(0.4-2.5 /xm)联合实验室-现场-航空光谱数据库，以评估回归模型对土壤-碳氢化合物混合物航空图像的性能。描述了样品制备和光谱测量。隐含的仪器是ASD FieldSpec Pro 2光谱仪和HySpex高光谱相机。显示了地面真实值与航空数据的一致性。显示了几个原始的室外光谱。
{"title":"Joint lab, field and airborne spectral database for the quantification of soil hydrocarbon content","authors":"V. Lever, P. Foucher, X. Briottet, D. Dubucq, R. Oltra-Carrió, L. Poutier, V. Achard, P. Déliot","doi":"10.1109/WHISPERS.2016.8071728","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071728","url":null,"abstract":"Soil-hydrocarbon mixtures give complex spectral responses. This has prohibited any physical modelling until now. Spectral analysis and quantification of contamination rate has been performed by regression models, calibrated on spectral databases. Only lab or field databases have been used. This study proposes an innovative joint lab-field-airborne spectral database in the reflective domain (0.4–2.5/xm) to assess the performance of regression models on airborne images of soil-hydrocarbon mixtures. Sample preparation and spectral measurements are described. Implied instruments are an ASD FieldSpec Pro 2 spectrometer and the HySpex hyperspectral camera. Accordance between ground truth and airborne data is shown. Several raw outdoor spectra are displayed.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124393184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071710
Pankaj H. Randhe, S. Durbha, N. Younan
Jetson TK1 is a recently launched embedded application development platform from NVIDIA, which features the Tegra K1 processor and Kepler Graphics Processing Unit (GPU). We envisage that such a system has huge potential for deploying an embedded system for on-board classification of hyperspectral images. We used a convolutional deep neural network for designing a unified model for hyperspectral image classification. Deep convolutional model hierarchically extracts spectral-spatial features from hyperspectral imagery and these features are used by the fully connected layer of neural network to perform pixel level classification of hyperspectral imagery. Our experimental results show that Jetson TK1 based hyperspectral image classification gives promising results and the possibility of having Jetson based embedded platform for on-board classification of hyperspectral images.
{"title":"Embedded high performance computing for on-board hyperspectral image classification","authors":"Pankaj H. Randhe, S. Durbha, N. Younan","doi":"10.1109/WHISPERS.2016.8071710","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071710","url":null,"abstract":"Jetson TK1 is a recently launched embedded application development platform from NVIDIA, which features the Tegra K1 processor and Kepler Graphics Processing Unit (GPU). We envisage that such a system has huge potential for deploying an embedded system for on-board classification of hyperspectral images. We used a convolutional deep neural network for designing a unified model for hyperspectral image classification. Deep convolutional model hierarchically extracts spectral-spatial features from hyperspectral imagery and these features are used by the fully connected layer of neural network to perform pixel level classification of hyperspectral imagery. Our experimental results show that Jetson TK1 based hyperspectral image classification gives promising results and the possibility of having Jetson based embedded platform for on-board classification of hyperspectral images.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115357006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071793
A. Psalta, V. Karathanassi, P. Kolokoussis
This paper aims at assessing the performance of the Simple Linear Iterative Clustering (SLIC) superpixel generating algorithm on hyperspectral images. Two modified versions of SLIC algorithm have been proposed. In the first, the HyperSLIC version, modifications were made to the basic algorithm in order to work with higher dimensions. In the second, the FD-SLIC version, a more complex distance measure, the fractional distance, already successfully used in the unmixing procedure was introduced. HyperSLIC was also applied on the abundance maps that are produced by the endmembers of the hyperspectral image. Algorithms have been applied on two images. Evaluation was based on visual inspection, NSE metric and “danger” maps. It has been shown that whole hyperspectral volume and fractional distance metric improves SLIC performance.
本文旨在评估简单线性迭代聚类(Simple Linear Iterative Clustering, SLIC)超像素生成算法在高光谱图像上的性能。提出了两个改进版本的SLIC算法。在第一个HyperSLIC版本中，为了处理更高的维度，对基本算法进行了修改。其次，介绍了FD-SLIC版本，一种更复杂的距离测量，分数距离，已经成功地应用于解混过程。hyperlic还应用于由高光谱图像的末端成员产生的丰度图。算法应用于两幅图像。评估基于目视检查、NSE度量和“危险”地图。研究表明，整体高光谱体积和分数距离度量提高了SLIC的性能。
{"title":"Modified versions of SLIC algorithm for generating superpixels in hyperspectral images","authors":"A. Psalta, V. Karathanassi, P. Kolokoussis","doi":"10.1109/WHISPERS.2016.8071793","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071793","url":null,"abstract":"This paper aims at assessing the performance of the Simple Linear Iterative Clustering (SLIC) superpixel generating algorithm on hyperspectral images. Two modified versions of SLIC algorithm have been proposed. In the first, the HyperSLIC version, modifications were made to the basic algorithm in order to work with higher dimensions. In the second, the FD-SLIC version, a more complex distance measure, the fractional distance, already successfully used in the unmixing procedure was introduced. HyperSLIC was also applied on the abundance maps that are produced by the endmembers of the hyperspectral image. Algorithms have been applied on two images. Evaluation was based on visual inspection, NSE metric and “danger” maps. It has been shown that whole hyperspectral volume and fractional distance metric improves SLIC performance.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071688
S. Livens, J. Blommaert, D. Nuyts, A. Sima, P. Baeck, B. Delauré
The COSI hyperspectral imaging system, suitable for small RPAS, is able to produce high resolution hyperspectral data products. By extensive inflight testing, we have identified the main challenges for achieving reliable high quality results. Based on these insights, we propose a refined radiometric calibration strategy. It uses a set of three reference targets, two grey and one colored target, which are to be measured inflight. We present on-ground measurements of the targets with COSI, as in flight measurements, demonstrating the merits of the approach are still ongoing.
{"title":"Radiometric calibration of the cosi hyperspectral RPAS camera","authors":"S. Livens, J. Blommaert, D. Nuyts, A. Sima, P. Baeck, B. Delauré","doi":"10.1109/WHISPERS.2016.8071688","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071688","url":null,"abstract":"The COSI hyperspectral imaging system, suitable for small RPAS, is able to produce high resolution hyperspectral data products. By extensive inflight testing, we have identified the main challenges for achieving reliable high quality results. Based on these insights, we propose a refined radiometric calibration strategy. It uses a set of three reference targets, two grey and one colored target, which are to be measured inflight. We present on-ground measurements of the targets with COSI, as in flight measurements, demonstrating the merits of the approach are still ongoing.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071707
K. Laakso, J. Peter, B. Rivard, R. Gloaguen
The most intense hydrothermally altered rocks in volcanogenic massive sulfide (VMS) deposit systems occur in the stratigraphically underlying feeder zone and rocks immediately adjacent to mineralization. This alteration zone is typically much larger than the mineralization itself, and hence the ability to detect such alteration by optical remote sensing can be invaluable for mineral exploration. Our investigation focuses on assessing the applicability of hyperspectral data to determine trends in hydrothermal alteration intensity in and around the Izok Lake VMS deposit in northern Canada. To this end, we linked hydrothermal alteration intensity information based on two indices, the Ishikawa (AI) and chlorite-carbonate-pyrite (CCPI), to hyperspectral field and laboratory data in three dimensions. Our results suggest that chlorite group minerals display variable chemical composition across the study area that broadly correlates with hydrothermal alteration intensity.
{"title":"Combined hyperspectral and lithogeochemical estimation of alteration intensities in a volcanogenic massive sulfide deposit hydrothermal system: A case study from Northern Canada","authors":"K. Laakso, J. Peter, B. Rivard, R. Gloaguen","doi":"10.1109/WHISPERS.2016.8071707","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071707","url":null,"abstract":"The most intense hydrothermally altered rocks in volcanogenic massive sulfide (VMS) deposit systems occur in the stratigraphically underlying feeder zone and rocks immediately adjacent to mineralization. This alteration zone is typically much larger than the mineralization itself, and hence the ability to detect such alteration by optical remote sensing can be invaluable for mineral exploration. Our investigation focuses on assessing the applicability of hyperspectral data to determine trends in hydrothermal alteration intensity in and around the Izok Lake VMS deposit in northern Canada. To this end, we linked hydrothermal alteration intensity information based on two indices, the Ishikawa (AI) and chlorite-carbonate-pyrite (CCPI), to hyperspectral field and laboratory data in three dimensions. Our results suggest that chlorite group minerals display variable chemical composition across the study area that broadly correlates with hydrothermal alteration intensity.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126857235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071712
M. Islam, Derek T. Anderson, J. Ball, N. Younan
Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract diverse features from hyperspectral imagery using different proximity measures and band grouping. The output is fed to ℓp-norm MKL for feature-level fusion, where larger p's are preferred for diverse vs sparse solutions. Preliminary results on benchmark data indicates that ℓp-norm MKSVM of diverse features and kernels leads to noticeable performance gain.
{"title":"Fusion of diverse features and kernels using LP-norm based multiple kernel learning in hyperspectral image processing","authors":"M. Islam, Derek T. Anderson, J. Ball, N. Younan","doi":"10.1109/WHISPERS.2016.8071712","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071712","url":null,"abstract":"Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract diverse features from hyperspectral imagery using different proximity measures and band grouping. The output is fed to ℓp-norm MKL for feature-level fusion, where larger p's are preferred for diverse vs sparse solutions. Preliminary results on benchmark data indicates that ℓp-norm MKSVM of diverse features and kernels leads to noticeable performance gain.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071692
D. Tratt, S. J. Young, P. Johnson, K. Buckland, D. Lynch
A multi-year study of ammonia emissions from a recently exposed geothermal fumarole field at the SE edge of the Salton Sea (Southern California) is described. The work makes extensive use of airborne thermal-infrared hyperspectral imagery acquired over the field site.
{"title":"Multi-year study of remotely-sensed ammonia emission from fumaroles in the salton sea geothermal field","authors":"D. Tratt, S. J. Young, P. Johnson, K. Buckland, D. Lynch","doi":"10.1109/WHISPERS.2016.8071692","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071692","url":null,"abstract":"A multi-year study of ammonia emissions from a recently exposed geothermal fumarole field at the SE edge of the Salton Sea (Southern California) is described. The work makes extensive use of airborne thermal-infrared hyperspectral imagery acquired over the field site.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121317113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071804
A. Fraeman, B. Ehlmann, G. Northwood-Smith, Yang Liu, M. Wadhwa, R. Greenberger
We use VSWIR microimaging spectroscopy to survey the spectral diversity of HED meteorites at 80-μm/pixel spatial scale. Our goal in this work is both to explore the emerging capabilities of microimaging VSWIR spectroscopy and to contribute to understanding the petrologic diversity of the HED suite and the evolution of Vesta. Using a combination of manual and automated hyperspectral classification techniques, we identify four major classes of materials based on VSWIR absorptions that include pyroxene, olivine, Fe-bearing feldspars, and glass-bearing/featureless materials. Results show microimaging spectroscopy is an effective method for rapidly and non-destructively characterizing small compositional variations of meteorite samples and for locating rare phases for possible follow-up investigation. Future work will include incorporating SEM/EDS results to quantify sources of spectral variability and placing observations within a broader geologic framework of the differentiation and evolution of Vesta.
{"title":"Using VSWIR microimaging spectroscopy to explore the mineralogical diversity of HED meteorites","authors":"A. Fraeman, B. Ehlmann, G. Northwood-Smith, Yang Liu, M. Wadhwa, R. Greenberger","doi":"10.1109/WHISPERS.2016.8071804","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071804","url":null,"abstract":"We use VSWIR microimaging spectroscopy to survey the spectral diversity of HED meteorites at 80-μm/pixel spatial scale. Our goal in this work is both to explore the emerging capabilities of microimaging VSWIR spectroscopy and to contribute to understanding the petrologic diversity of the HED suite and the evolution of Vesta. Using a combination of manual and automated hyperspectral classification techniques, we identify four major classes of materials based on VSWIR absorptions that include pyroxene, olivine, Fe-bearing feldspars, and glass-bearing/featureless materials. Results show microimaging spectroscopy is an effective method for rapidly and non-destructively characterizing small compositional variations of meteorite samples and for locating rare phases for possible follow-up investigation. Future work will include incorporating SEM/EDS results to quantify sources of spectral variability and placing observations within a broader geologic framework of the differentiation and evolution of Vesta.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116313988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071721
Jan Koenig, L. Gueguen
In advance of releasing a WorldView-3 (WV-3) dataset with both VNIR and SWIR bands for research purposes, this study was conducted to provide a baseline comparison of land use/land cover (LULC) classification based on hyperspectral and 16-, 8-, and 4-bands of WV-3 imagery. We chose a well-researched area over the city center of Pavia, Italy. Results suggest that the addition of spectral information from WV-3's SWIR bands helps bridge the gap between precision/recall scores obtained with multispectral VNIR vs. hyperspectral VNIR imagery.
{"title":"A comparison of land use land cover classification using superspectral WorldView-3 vs hyperspectral imagery","authors":"Jan Koenig, L. Gueguen","doi":"10.1109/WHISPERS.2016.8071721","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071721","url":null,"abstract":"In advance of releasing a WorldView-3 (WV-3) dataset with both VNIR and SWIR bands for research purposes, this study was conducted to provide a baseline comparison of land use/land cover (LULC) classification based on hyperspectral and 16-, 8-, and 4-bands of WV-3 imagery. We chose a well-researched area over the city center of Pavia, Italy. Results suggest that the addition of spectral information from WV-3's SWIR bands helps bridge the gap between precision/recall scores obtained with multispectral VNIR vs. hyperspectral VNIR imagery.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124618204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071774
E. Leask, B. Ehlmann
Visible-shortwave infrared microimaging reflectance spectroscopy is a new technique to identify minerals, quantify abundances, and assess textural relationships at sub-millimetre scale without destructive sample preparation. Here we used a prototype instrument to image serpentinized igneous rocks and carbonate-rich travertine deposits to evaluate performance, relative to traditional techniques: XRD (mineralogical analysis of bulk powders with no texture preservation) and SEM/EDS (analysis of phases and textures using chemical data from polished thin sections). VSWIR microimaging spectroscopy is ideal for identifying spatially coherent rare phases, below XRD detection limits. The progress of alteration can also be inferred from spectral parameters and may correspond to phases that are amorphous in XRD. However, VSWIR microimaging spectroscopy can sometimes be challenging with analyses of very dark materials (reflectance <0.05) and mineral mixtures occurring at a spatial scales multiple factors below the pixel size. Abundances derived from linear unmixing typically agree with those from XRD and EDS within ∼10%.
{"title":"Identifying and quantifying mineral abundance through VSWIR microimaging spectroscopy: A comparison to XRD and SEM","authors":"E. Leask, B. Ehlmann","doi":"10.1109/WHISPERS.2016.8071774","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071774","url":null,"abstract":"Visible-shortwave infrared microimaging reflectance spectroscopy is a new technique to identify minerals, quantify abundances, and assess textural relationships at sub-millimetre scale without destructive sample preparation. Here we used a prototype instrument to image serpentinized igneous rocks and carbonate-rich travertine deposits to evaluate performance, relative to traditional techniques: XRD (mineralogical analysis of bulk powders with no texture preservation) and SEM/EDS (analysis of phases and textures using chemical data from polished thin sections). VSWIR microimaging spectroscopy is ideal for identifying spatially coherent rare phases, below XRD detection limits. The progress of alteration can also be inferred from spectral parameters and may correspond to phases that are amorphous in XRD. However, VSWIR microimaging spectroscopy can sometimes be challenging with analyses of very dark materials (reflectance <0.05) and mineral mixtures occurring at a spatial scales multiple factors below the pixel size. Abundances derived from linear unmixing typically agree with those from XRD and EDS within ∼10%.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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