Geometric Calibration and Radiometric Correction of the Maia Multispectral Camera

Nocerino, Erica; Dubbini, Marco; Menna, Fabio; Remondino, Fabio; Gattelli, Mario; Covi, D.

doi:10.5194/isprs-archives-xlii-3-w3-149-2017

Cited by 25 publications

(20 citation statements)

References 19 publications

(14 reference statements)

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“…Suomalainen et al [40] and Lucieer et al [38] performed non-uniformity normalization for their pushbroom systems by taking a series of images of a large integrating sphere illuminated with a quartz-tungsten-halogen lamp. Kelcey and Lucieer [50] and Nocerino et al [135] determined a per-pixel correction factor look-up-table (LUT) using a uniform, spectrally homogeneous, Lambertian flat field surface for the mini-MCA and the MAIA multispectral cameras, respectively. Aasen et al [127], Büttner and Röser [126], and Yang et al [129] used an integrating sphere to perform the non-uniformity normalization and determined the sensor's linear response range by measuring at different integration times.…”

Section: Relative Radiometric Calibrationmentioning

confidence: 99%

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

et al. 2018

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In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

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Section: Relative Radiometric Calibrationmentioning

confidence: 99%

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

et al. 2018

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“…Multispectral imagery was acquired throughout the UAV flights using the MAIA camera system (SAL Engineering/EOPTIS), which is composed of an array of nine monochromatic sensors, each having a 1.2 Mpixel resolution. Furthermore, the nine sensors of the MAIA camera (referred to hereafter as the MAIA/Sentinel-2) have band-pass filters that have the same central wavelength and width as that of the first nine bands (i.e., bands 1 to 8A, Table 2) of the ESA Sentinel-2 multispectral instrument [37]. During the UAV flights, these sensors imaged the trial plots from a fixed nadir position with the aid of a three-axis stabilisation gimbal (DJI Ronin-MX).…”

Section: Uav Platform and Multispectral Instrumentmentioning

confidence: 99%

“…This target reflectance was converted to absolute reflectance, following the guidelines outlined by the National Environment Research Council (NERC) Field Spectroscopy Facility [40]. These data were then convolved with the spectral response of each MAIA/Sentinel-2 band to correct the reflectance digital number (DN) recorded at pixels for each of the MAIA/Sentinel-2 spectral bands to absolute reflectance [37]:…”

Section: Data Post-processingmentioning

confidence: 99%

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

et al. 2019

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Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite-delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres-could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements-the mean R 2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R 2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two-the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices. the value of LAI data for updating state variables in process-based agroecosystem models in order to improve estimates of crop yield [3][4][5][6] and land-atmosphere carbon dioxide exchanges [7,8]. On the other hand, chlorophyll is a key driver of plant light absorption and conversion to chemical energy and is, therefore, an indicator of plant health and potential gross primary productivity [9,10]. In particular, leaf chlorophyll content is linked to leaf photosynthetic capacity via the maximum rate of carboxylation (V max ). The Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme, which relates to V max and leaf-level carbon fixation, correlates to leaf nitrogen (N) content [11]. Since leaf N also consists of chlorophyll, plant chlorophyll is strongly correlated to leaf N [12][13][14] including that for winter wheat [15].LAI and chlorophyll content are important factors determining crop reflectance [11] and can, therefore, be estimated from optical Earth observation satellit...

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“…Lucieer [34,35] and Del Pozo et al [36] proposed a workflow of the image-based sensor corrections of a Mini-MCA 6 UAV multispectral camera with a rolling shutter sensor. Nocerino et al [37] presented the geometric and radiometric calibration workflow for an MAIA multispectral camera. Padró et al [38] used empirical line vicarious calibration for MicaSense RedEge and for Parrot Sequoia [39], being the widely used commercial UAV multispectral sensors.…”

Section: Introductionmentioning

confidence: 99%

Radiometric and Atmospheric Corrections of Multispectral μMCA Camera for UAV Spectroscopy

2019

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This study presents a complex empirical image-based radiometric calibration method for a Tetracam μMCA multispectral frame camera. The workflow is based on a laboratory investigation of the camera’s radiometric properties combined with vicarious atmospheric correction using an empirical line. The effect of the correction is demonstrated on out-of-laboratory field campaign data. The dark signal noise behaviour was investigated based on the exposure time and ambient temperature. The vignette effect coupled with nonuniform quantum efficiency was studied with respect to changing exposure times and illuminations to simulate field campaign conditions. The efficiency of the proposed correction workflow was validated by comparing the reflectance values that were extracted from a fully corrected image and the raw data of the reference spectroscopy measurement using three control targets. The Normalized Root Mean Square Errors (NRMSE) of all separate bands ranged from 0.24 to 2.10%, resulting in a significant improvement of the NRMSE compared to the raw data. The results of a field experiment demonstrated that the proposed correction workflow significantly improves the quality of multispectral imagery. The workflow was designed to be applicable to the out-of-laboratory conditions of UAV imaging campaigns in variable natural conditions and other types of multiarray imaging systems.

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Geometric Calibration and Radiometric Correction of the Maia Multispectral Camera

Cited by 25 publications

References 19 publications

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

Radiometric and Atmospheric Corrections of Multispectral μMCA Camera for UAV Spectroscopy

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