Modeling of radial asymmetry in lens distortion facilitated by modern optimization techniques

Villiers, J. P. de; Leuschner, F.W.; Geldenhuys, R.

doi:10.1117/12.838804

Cited by 5 publications

(4 citation statements)

References 5 publications

(2 reference statements)

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“…Pre-processing of the image: We pre-processed the imagery by conducting: (1) noise elimination [28], (2) vignette reduction [28], (3) lens distortion correction [29][30][31], (4) band registration [32], (5) radiometric calibration [33], and (6) background removal [34]. We set 30 ground control points in the experimental area, and used X900 GNSS to determine the geographic coordinates.…”

Section: The Uav Systemmentioning

confidence: 99%

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

et al. 2017

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Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)-derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400-800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI 2 ) has better accuracy with higher coefficient of determination (R c 2 = 0.79, R v 2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture.

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Section: The Uav Systemmentioning

confidence: 99%

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

et al. 2017

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“…A commonly adopted model for lens distortion is the Brown-Conrady distortion model [35,38,39]. The Brown-Conrady model is capable of calculating both the radial and tangential components of lens distortion.…”

Section: Brown-conrady Modelmentioning

confidence: 99%

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

2012

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Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown-Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.

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“…Lens distortion was corrected using the Brown model [21]. The lens distortion correction parameters for each channel were measured using a program supplied by the Beijing Spatial Information Technology Co., Ltd., and the coefficients are given in Table 3.…”

Section: Methodsmentioning

confidence: 99%

“…Lens distortion is mainly caused by the differences in magnification across a lens surface and the misalignment between the lens and the detector plane, represented by radial distortion and tangential distortion [20]. Generally, correction coefficients are calculated to correct for noise and vignetting in multispectral images [15], and the Brown model is commonly adopted for lens distortion correction [15,21,22]. In addition to the preliminary corrections, band registration is required to improve spatial consistency between bands.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of the Image Preprocessing Process of a Six-Band Multispectral Camera Mounted on an Unmanned Aerial Vehicle for Winter Wheat Monitoring

Jiang

Zheng

et al. 2019

Sensors

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Unmanned aerial vehicle (UAV)-based multispectral sensors have great potential in crop monitoring due to their high flexibility, high spatial resolution, and ease of operation. Image preprocessing, however, is a prerequisite to make full use of the acquired high-quality data in practical applications. Most crop monitoring studies have focused on specific procedures or applications, and there has been little attempt to examine the accuracy of the data preprocessing steps. This study focuses on the preprocessing process of a six-band multispectral camera (Mini-MCA6) mounted on UAVs. First, we have quantified and analyzed the components of sensor error, including noise, vignetting, and lens distortion. Next, different methods of spectral band registration and radiometric correction were evaluated. Then, an appropriate image preprocessing process was proposed. Finally, the applicability and potential for crop monitoring were assessed in terms of accuracy by measurement of the leaf area index (LAI) and the leaf biomass inversion under variable growth conditions during five critical growth stages of winter wheat. The results show that noise and vignetting could be effectively removed via use of correction coefficients in image processing. The widely used Brown model was suitable for lens distortion correction of a Mini-MCA6. Band registration based on ground control points (GCPs) (Root-Mean-Square Error, RMSE = 1.02 pixels) was superior to that using PixelWrench2 (PW2) software (RMSE = 1.82 pixels). For radiometric correction, the accuracy of the empirical linear correction (ELC) method was significantly higher than that of light intensity sensor correction (ILSC) method. The multispectral images that were processed using optimal correction methods were demonstrated to be reliable for estimating LAI and leaf biomass. This study provides a feasible and semi-automatic image preprocessing process for a UAV-based Mini-MCA6, which also serves as a reference for other array-type multispectral sensors. Moreover, the high-quality data generated in this study may stimulate increased interest in remote high-efficiency monitoring of crop growth status.

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Modeling of radial asymmetry in lens distortion facilitated by modern optimization techniques

Cited by 5 publications

References 5 publications

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

Analysis and Evaluation of the Image Preprocessing Process of a Six-Band Multispectral Camera Mounted on an Unmanned Aerial Vehicle for Winter Wheat Monitoring

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