An initial exploration of vicarious and in-scene calibration techniques for small unmanned aircraft systems

Mamaghani, Baabak; Sasaki, G.; Connal, Ryan; Kha, Kevin; Knappen, Jackson; Hartzell, R.A.; Marcellus, Evan D.; Bauch, Timothy; Raqueño, Nina G.; Salvaggio, Carl

doi:10.1117/12.2302744

Cited by 13 publications

(21 citation statements)

References 6 publications

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“…Current literature on testing its radiometric correction has been limited. We found one study proposed a similar yet simpler approach named At-Altitude Radiance Ratio (AARR), which basically defined the ratio of the DLS and CRP radiance as the correction factor [20]. As shown in Figure 4 of our study, the relationship between DLS and CRP radiance in each band is not a simple ratio; it contains a small yet positive intercept value.…”

Section: Discussionmentioning

confidence: 82%

“…The current procedure of RedEdge image correction in common drone data processing packages (e.g., Pix4DMapper) mostly utilizes the CRP panel itself [19]. However, some studies have reported the relatively higher percent errors of its radiometric calibration [15,20]. Our recent practices in a woodland using a RedEdge-M camera [3] also suggested poorly calibrated reflectance, which resulted in overwhelmingly high NDVI of tree canopies.…”

Section: Introductionmentioning

confidence: 99%

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At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping

Wang

2021

Sensors

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Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.

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Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping

Wang

2021

Sensors

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“…RSR curves were computed using a method similar to the one described by Mamaghani et al [33]. The RSR determination methodology is shown in Equations (11)–(13).…”

Section: Methodsmentioning

confidence: 99%

“…AARR produces a reflectance image by dividing each spectral band radiance image by the corresponding band-effective downwelling radiance. Equation (27) demonstrates this process [33]. ρi=Ls,iDLSi DLSi=Esolar,i′πcos(σ′)τi′+L↓solar,i where DLSi is the downwelling light sensor radiance recorded by the MicaSense RedEdge, ρi is the reflectance factor, Ls,i is the band effective spectral radiance, Esolar,i′ is the spectral exoatmospheric solar irradiance, σ′ is the solar zenith angle, τi′ is the spectral transmission from space to the sUAS, L↓solar,i is the solar scattered downwelling sky radiance propagating o...…”

Section: Methodsmentioning

confidence: 99%

Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Mamaghani

Salvaggio

2019

Sensors

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This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with “factory” radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their “factory” parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

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“…MicaSense proposes a method for conversion of the raw pixel values into absolute spectral radiance values using the onboard Downwelling Light Sensor (DLS) which provides irradiance values for each band (MicaSense, 2017). Mamaghani et al (2018) proposed a new technique making use of the supplied DLS sensor of the MicaSense RedEdge camera, and an improved laboratory method (Mamaghani et al, 2018;Mamaghani and Salvaggio, 2019).…”

Section: Introductionmentioning

confidence: 99%

Preliminary Investigation Into the Geometric Calibration of the Micasense Rededge-M Multispectral Camera

Frontera

Smith

Marsh

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Multispectral cameras, in the past the prerogative of Remote Sensing (RS) applications via satellites and manned aircraft, are becoming increasingly used in photogrammetric applications. Moreover, the ubiquitous use of Unmanned Aerial Vehicles (UAVs) has created a need for the miniaturisation of sensors, which has contributed to the availability of a wide range of relatively low-cost and lightweight cameras. Therefore, small multispectral cameras mounted on UAVs provide an effective and low-cost solution when it comes to acquiring airborne radiometric data.With the growing interest for such sensors to perform photogrammetric tasks, camera calibration remains an essential step in order to obtain reliable and geometrically accurate information.This paper will investigate the camera calibration parameters between the five bands of the MicaSense RedEdge-M sensor from laboratory trials. The results of the camera calibration will be obtained from the use, primarily, of Australis software and a calibration frame within the Nottingham Geospatial Institute. The variations of the parameters demonstrate the need for distortion correction separately within each band before using the images for photogrammetry.

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An initial exploration of vicarious and in-scene calibration techniques for small unmanned aircraft systems

Cited by 13 publications

References 6 publications

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping

Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Preliminary Investigation Into the Geometric Calibration of the Micasense Rededge-M Multispectral Camera

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