Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

Xu, Kaiqiu; Gong, Yi; Fang, Shenghui; Wang, Ke; Lin, Zhiheng; Wei, Feng

doi:10.3390/rs11111291

Cited by 26 publications

(13 citation statements)

References 34 publications

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“…White plastic boards, as the pseudo targets and radiometric calibration targets, were used to convert DN to spectral reflectance using the calibration equation. Xu et al [154] emphasized that the ELM independently calibrate each channel, ignoring the correlation between spectral bands. The authors proposed a spectral angle constraint method (SACM) for the radiometric processing of all bands as a whole.…”

Section: Quality Assurance Metrics For Radiometric Datamentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

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Section: Quality Assurance Metrics For Radiometric Datamentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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“…In addition, radiometric calibration of the images is needed to convert the raw digital numbers (DNs) into reflectance values. To do this, calibration targets and field campaigns are essential to have a good approximation of the backscattered radiance of the various targets observed in the orthophotos [17,18].…”

Section: Introductionmentioning

confidence: 99%

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Αγαπίου

2020

Drones

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Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

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“…The flight altitudes of small UAS (sUAS) typically do not exceed 250 m due to governmental restrictions on UAS operations [34]. As a result, numerous UAS studies have implemented an empirical line method (ELM) for radiometric calibration of UAS imagery [35][36][37][38][39][40][41][42][43][44][45][46][47]. The foundation of an ELM is the regression of ground-based spectroradiometer measurements and airborne remotely sensed measurements of a spectrally stable calibration target to develop calibration equations that convert image digital numbers (DNs) to physical at-surface reflectance units [48].…”

Section: Introductionmentioning

confidence: 99%

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

Zarzar

Dash

Dyer

et al. 2020

Drones

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The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.

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Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

Cited by 26 publications

References 34 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

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