Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis

Buckley, Simon J.; Kurz, Tobias H.; Howell, John; Schneider, D.

doi:10.1016/j.cageo.2013.01.018

Cited by 103 publications

(65 citation statements)

References 30 publications

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“…Terrestrial imaging spectrometry using a close-range instrument (<100 m scanning distance) with near-horizontal viewing direction has recently gained attention for mapping of minerals in near-vertical outcrops [35][36][37][38]. Different distances from sensor to target may comprise a continuum of spatial resolutions, and thus calculating the real distribution of minerals mapped from the hyperspectral imagery demands co-registering the data with geometric information, such as that derived from digital photogrammetry or LiDAR scanning [39][40][41][42][43]. The integration of geometrically accurate terrain/topography data and spectral information can significantly improve collection of geological data to distinguish between lithologies and barren rock from potential economic ore deposits.…”

Section: Introductionmentioning

confidence: 99%

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

et al. 2018

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Abstract:Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches.

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Section: Introductionmentioning

confidence: 99%

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

et al. 2018

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“…Finally, space resection was used to solve for the unknown position and orientation of the hyperspectral image using the conjugate image and 3D coordinates as input . Subsequently, fusion products could be created as in Buckley et al [2013].…”

Section: Terrestrial Laser Scanning and Hyperspectral Image Registrationmentioning

confidence: 99%

“…As this approach is not based on statistical modelling, and although individual layers may contain higher or lower metal contents, the areas indicated by these thematic maps suggest a first screening to identify the most promising parts of the exposed area with respect to the general goal of identifying material that is economically viable for recycling. For visualisation of the results, singular MNF bands or bands composites, classification results and colour-coded combined classes can be integrated into the lidar model as new texture layers, as in Buckley et al [2013]. Figure 10 gives an example of the data integration and shows a visualisation of the lidar model with integrated RGB photographs, a MNF band composite (Fig.…”

Section: Image Classificationmentioning

confidence: 99%

Mapping of iron and steelwork by-products using close range hyperspectral imaging: A case study in Thuringia, Germany

Denk

Gläßer

Kurz

et al. 2015

European Journal of Remote Sensing

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This paper presents the first use of close range terrestrial hyperspectral imaging to explore by-products from the iron and steel industry at a dump site cross section, using established mapping approaches in geological remote sensing. Laboratory reflectance measurements, X-Ray diffraction and chemical analysis provided a benchmark for the imaging results. In addition, terrestrial laser scanning was applied to create a high resolution 3D model of the studied section, for hyperspectral image and classification integration. Results demonstrate the applicability of spectral methods for remote discrimination and mapping of materials from iron and steel production which are of potential economic interest. This will aid the appraisal of resource distribution for urban mining and material recycling.

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“…Buckley et al (2013) combined hyperspectral and LiDAR data for geological out crop analysis, floodplain classification (Verrelst et al, 2009) and civil engineering structural monitoring (Brook et al, 2010). Most of the other work on LiDAR and imaging spectroscopy data fusion (Colgan et al, 2012;Dalponte et al, 2012;Ghosh et al, 2013;Jones et al, 2010); focus on vegetation applications.…”

Section: Ajasmentioning

confidence: 99%

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

Idrees

Shafri

Saeidi

2013

American Journal of Applied Sciences

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This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988.

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Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis

Cited by 103 publications

References 30 publications

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

Mapping of iron and steelwork by-products using close range hyperspectral imaging: A case study in Thuringia, Germany

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

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