Close-range hyperspectral imaging for geological field studies: workflow and methods

Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional (3D) models, derived from terrestrial laser scanning (lidar), that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.

[1]  Derek M. Rogge,et al.  Mapping lithology in Canada's Arctic: application of hyperspectral data using the minimum noise fraction transformation and matched filtering , 2005 .

[2]  S. Buckley,et al.  Terrestrial laser scanning in geology: data acquisition, processing and accuracy considerations , 2008, Journal of the Geological Society.

[3]  R. Clark,et al.  High spectral resolution reflectance spectroscopy of minerals , 1990 .

[4]  Fred A. Kruse The Effects of Spatial Resolution, Spectral Resolution, and SNR on Geologic Mapping Using Hyperspectral Data, Northern Grapevine Mountains, Nevada , 2000 .

[5]  John A. Howell,et al.  From outcrop to reservoir simulation model: Workflow and procedures , 2007 .

[6]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[7]  S. Gerstl,et al.  Radiation physics and modelling for off-nadir satellite sensing of non-Lambertian surfaces , 1986 .

[8]  H. Maas,et al.  A geometric model for linear‐array‐based terrestrial panoramic cameras , 2006 .

[9]  Warren B. Cohen,et al.  Empirical methods to compensate for a view-angle-dependent brightness gradient in AVIRIS imagery☆ , 1997 .

[10]  C. Maierhofer,et al.  MULTI-SPECTRAL DATA ACQUISITION AND PROCESSING TECHNIQUES FOR DAMAGE DETECTION ON BUILDING SURFACES , 2006 .

[11]  R. Gawthorpe,et al.  Stratigraphic control on laterally persistent cementation, Book Cliffs, Utah , 1995, Journal of the Geological Society.

[12]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .

[13]  P. Atkinson,et al.  Interpreting image-based methods for estimating the signal-to-noise ratio , 2005 .

[14]  John Thurmond,et al.  Hyperspectral image analysis of different carbonate lithologies (limestone, karst and hydrothermal dolomites): the Pozalagua Quarry case study (Cantabria, North‐west Spain) , 2012 .

[15]  J. Boardman,et al.  Leveraging the High Dimensionality of AVIRIS Data for improved Sub-Pixel Target i Unmixing and Rejection of False Positives : Mixture Tuned Matched Filtering , 1998 .

[16]  R. Graham Electromagnetic radiation; the communication link in remote sensing , 1980 .

[17]  R. Richter,et al.  Correction of satellite imagery over mountainous terrain. , 1998, Applied optics.

[18]  J. R. Jensen Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[19]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[20]  J. Salisbury,et al.  Visible and near infrared spectra of minerals and rocks. VI. Additional silicates , 1973 .

[21]  Stefano Girardi,et al.  Discrimination between marls and limestones using intensity data from terrestrial laser scanner , 2009 .

[22]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[23]  R. Clark,et al.  The U. S. Geological Survey, Digital Spectral Library: Version 1 (0.2 to 3.0um) , 1993 .

[24]  John W. Salisbury,et al.  Visible and near infrared spectra of minerals and rocks. II. Carbonates , 1971 .

[25]  C. Kerans,et al.  Digital Outcrop Models: Applications of Terrestrial Scanning Lidar Technology in Stratigraphic Modeling , 2005 .

[26]  D. Lichti Spectral Filtering and Classification of Terrestrial Laser Scanner Point Clouds , 2005 .

[27]  F. D. van der Meer,et al.  Spectral reflectance of carbonate mineral mixtures and bidirectional reflectance theory: Quantitative analysis techniques for application in remote sensing , 1995 .

[28]  Jens Nieke,et al.  Uniformity of Imaging Spectrometry Data Products , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[29]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[30]  Richard A. Beck,et al.  Analysis of hyperspectral and lidar data: Remote optical mineralogy and fracture identification , 2007 .

[31]  P. K Varshney,et al.  Advanced image processing techniques for remotely sensed hyperspectral data : with 128 figures and 30 tables , 2004 .

[32]  Kenneth Watson,et al.  Processing remote sensing images using the 2-D FFT; noise reduction and other applications , 1993 .

[33]  Roger N. Clark,et al.  The US Geological Survey, digital spectral reflectance library: version 1: 0.2 to 3.0 microns , 1993 .

[34]  Fabio Remondino,et al.  Image‐based 3D Modelling: A Review , 2006 .

[35]  Danilo Schneider,et al.  Integration of panoramic hyperspectral imaging with terrestrial lidar data , 2011 .

[36]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[37]  J. Howell,et al.  Depositional and Stratigraphic Architecture of the Santonian Emery Sandstone of the Mancos Shale: Implications for Late Cretaceous Evolution of the Western Interior Foreland Basin of Central Utah, U.S.A. , 2005 .

[38]  I. Trinks,et al.  Unlocking the spatial dimension: digital technologies and the future of geoscience fieldwork , 2005, Journal of the Geological Society.

[39]  R. Gawthorpe,et al.  Carbonate Cementation in a Sequence-Stratigraphic Framework: Upper Cretaceous Sandstones, Book Cliffs, Utah-Colorado , 2000 .

[40]  Fred A. Kruse,et al.  Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping , 2003, IEEE Trans. Geosci. Remote. Sens..

[41]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[42]  Christian Carlsson,et al.  Terrestrial Laser Scanning for use in Virtual Outcrop Geology , 2010 .