Detection of REEs with lightweight UAV-based hyperspectral imaging

Booysen, René; Jackisch, Robert; Lorenz, Sandra; Zimmermann, Robert; Kirsch, Moritz; Nex, Paul A. M.; Gloaguen, Richard

doi:10.1038/s41598-020-74422-0

Cited by 56 publications

(26 citation statements)

References 35 publications

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“…Their results showed with many samples over 50% deviations between airborne and field reflectance values, indicating some issues in the image calibration or the ELM reflectance conversion process. However, the spectral shapes produced using their method were reasonably good (R 2 : 0.85-0.99) and in the related article (Booysen et al, 2020) they showed that the method is good enough for mapping of rare earth elements. Minařík et al (2019) carried out radiometric calibration method based on a laboratory radiometric properties and vicarious correction using an empirical line for a Tetracam μMCA multispectral camera.…”

Section: Discussionmentioning

confidence: 99%

Direct reflectance transformation methodology for drone-based hyperspectral imaging

Suomalainen

Oliveira

Hakala

et al. 2021

Remote Sensing of Environment

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Multi-and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70 • . The system's lowaltitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100-150 m altitude, the NRMSEs without atmospheric correction were within 1.4%-8.7% for VIS bands and 2.0%-18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%-2.6% for VIS bands and to 2.3%-5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications.

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Section: Discussionmentioning

confidence: 99%

Direct reflectance transformation methodology for drone-based hyperspectral imaging

Suomalainen

Oliveira

Hakala

et al. 2021

Remote Sensing of Environment

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“…Another development trend of UAV magnetic survey is the use of larger numbers or types of sensors, which means that more information, including gradient tensor, can be obtained [131]. In addition, more information about the research area can be obtained by carrying spectrometers [132,133], electromagnetic sensors [26], [134], cameras [135][136][137], and gamma spectrometers [27,138] with UAVs. Although it is not introduced in this paper, it is important to have a view of these studies.…”

Section: Tendencies Of Uav Magnetic Surveysmentioning

confidence: 99%

Unmanned Aerial Vehicles for Magnetic Surveys: A Review on Platform Selection and Interference Suppression

Zheng

Xing

et al. 2021

Drones

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In the past two decades, unmanned aerial vehicles (UAVs) have been used in many scientific research fields for various applications. In particular, the use of UAVs for magnetic surveys has become a hot spot and is expected to be actively applied in the future. A considerable amount of literature has been published on the use of UAVs for magnetic surveys, however, how to choose the platform and reduce the interference of UAV to the collected data have not been discussed systematically. There are two primary aims of this study: (1) To ascertain the basis of UAV platform selection and (2) to investigate the characteristics and suppression methods of UAV magnetic interference. Systematic reviews were performed to summarize the results of 70 academic studies (from 2005 to 2021) and outline the research tendencies for applying UAVs in magnetic surveys. This study found that multi-rotor UAVs have become the most widely used type of UAVs in recent years because of their advantages such as easiness to operate, low cost, and the ability of flying at a very low altitude, despite their late appearance. With the improvement of the payload capacity of UAVs, to use multiple magnetometers becomes popular since it can provide more abundant information. In addition, this study also found that the most commonly used method to reduce the effects of the UAV’s magnetic interference is to increase the distance between the sensors and the UAV, although this method will bring about other problems, e.g., the directional and positional errors of sensors caused by erratic movements, the increased risk of impact to the magnetometers. The pros and cons of different types of UAV, magnetic interference characteristics and suppression methods based on traditional aeromagnetic compensation and other methods are discussed in detail. This study contributes to the classification of current UAV applications as well as the data processing methods in magnetic surveys.

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“…Tough terrain or outcrops that are difficult to reach on foot or by cars may be quickly surveyed with UAVs from a safe distance with limited manpower on-site, ensuring protection, speed, and quality. The importance of using UAV-based hyperspectral images for geological exploration mapping has been shown by limited studies (Booysen et al, 2020). Hyperspectral sensors have recently begun to be installed on unmanned aerial systems by the Helmholtz Institute Freiberg for Resource Technologies (Jakob et al, 2017).…”

Section: Drone-based Datamentioning

confidence: 99%

A review of machine learning in processing remote sensing data for mineral exploration

Shirmard,

Farahbakhsh,

Muller

et al. 2021

Preprint

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As a primary step in mineral exploration, a variety of features are mapped such as lithological units, alteration types, structures, and minerals. These features are extracted to aid decision-making in targeting ore deposits. Different types of remote sensing data including satellite optical and radar, airborne, and drone-based data make it possible to overcome problems associated with mapping these important parameters on the field. The rapid increase in the volume of remote sensing data obtained from different platforms has allowed scientists to develop advanced, innovative, and powerful data processing methodologies. Machine learning methods can help in processing a wide range of remote sensing data and in determining the relationship between the reflectance continuum and features of interest. Moreover, these methods are robust in processing spectral and ground truth measurements

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Detection of REEs with lightweight UAV-based hyperspectral imaging

Cited by 56 publications

References 35 publications

Direct reflectance transformation methodology for drone-based hyperspectral imaging

Direct reflectance transformation methodology for drone-based hyperspectral imaging

Unmanned Aerial Vehicles for Magnetic Surveys: A Review on Platform Selection and Interference Suppression

A review of machine learning in processing remote sensing data for mineral exploration

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