Hyperspectral outcrop models for palaeoseismic studies

Kirsch, Moritz; Lorenz, Sandra; Zimmermann, Robert; Andreani, Louis; Tuşa, Laura; Pospiech, Solveig; Jackisch, Robert; Khodadadzadeh, Mahdi; Ghamisi, Pedram; Unger, Gabriel; Hödl, Philip; Gloaguen, Richard; Middleton, Maarit; Sutinen, Raimo; Ojala, Antti E. K.; Mattila, Jussi; Nordbäck, Nicklas; Palmu, Jukka‐Pekka; Tiljander, Mia; Ruskeeniemi, Timo

doi:10.1111/phor.12300

Cited by 17 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been shown that LiDAR-derived terrain information can be used to correct geometric distortions in the hyperspectral data [27,28], and also to enhance geological interpretation [29,30,31]. Similarly, Salehi (2018), and Kirsch ( , 2019 [32,33,34,35] proposed workflows that integrate spectral products with 3D photogrammetric terrain data, demonstrating the advantages of terrestrial hyperspectral sensing for mapping vertical geological outcrops in extreme regions not observable by air or spaceborne sensors. Ghamisi (2018) [36] surveyed and analysed other classification techniques, such as those based on mathematical morphology, Markov random fields, tree partitioning and sparse representation.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

Windrim

Melkumyan

Murphy

et al. 2023

Geoscience Frontiers

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

Windrim

Melkumyan

Murphy

et al. 2023

Geoscience Frontiers

View full text Add to dashboard Cite

“…The area has a rich mining history dating back to the Bronze Age, while currently, significant resources remain and mining operations still take place. Panoramic outcrop scans were acquired by an AISA-FENIX camera, mounted on a tripod [34]. The captured HSI is composed of 300 × 1416 pixels and 190 spectral bands, covering the range between 0.38 and 2.50 µm.…”

Section: The Geological Spain Datasetmentioning

confidence: 99%

MS$^{2}$A-Net: Multiscale Spectral–Spatial Association Network for Hyperspectral Image Clustering

Shahi

Ghamisi

Rasti

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Remote sensing hyperspectral cameras acquire high spectral-resolution data that reveal valuable composition information on the targets (e.g., for Earth observation and environmental applications). The intrinsic high dimensionality and the lack of sufficient numbers of labeled/training samples prevent efficient processing of hyperspectral images (HSIs). HSI clustering can alleviate these limitations. In this study, we propose a multi-scale spectral-spatial association network (MS 2 A-Net) to cluster HSIs. The backbone of MS 2 A-Net is an autoencoder architecture that allows the network to capture the non-linear relation between data points in an unsupervised manner. The network applies a multi-stream approach. One stream extracts spectral information by deploying a spectral association unit. The other stream derives multi-scale contextual and spatial information by employing dilated (atrous) convolutional kernels. The obtained feature representation generated by MS 2 A-Net is fed into a standard k-means clustering algorithm to produce the final clustering result. Extensive experiments on four HSIs for different types of applications (i.e., geological-, rural-, and urbanmapping) demonstrate the superior performance of MS 2 A-Net over the state-of-the-art shallow/deep learning-based clustering approaches in terms of clustering accuracy. The code is available at: https://github.com/Kasra2020/MS2A-Net.

show abstract

“…In this case study, the following surface coverages were achieved per sensor: The used UAS-fitted workflows are matured to a high user friendliness and could be flexibly adapted to all mining and exploration scenarios, where high resolution and spatial coverage is required. Safety concerns for detailed mapping along pit walls are mitigated by UAS mapping, when used for vertical outcrop scanning along unstable wall sections [67].…”

Section: Assessing the General Uas Survey Workflow With Focus On Imagmentioning

confidence: 99%

Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.

show abstract

Hyperspectral outcrop models for palaeoseismic studies

Abstract: The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by

Cited by 17 publications

References 42 publications

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

MS$^{2}$A-Net: Multiscale Spectral–Spatial Association Network for Hyperspectral Image Clustering

Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

Contact Info

Product

Resources

About