Comparing, validating and improving the performance of reflectance obtention method for UAV-Remote sensing

Cao, Hongtao; Gu, Xingfa; Sun, Yuan; Gao, Hongli; Tao, Zui; Shi, Shuaiyi

doi:10.1016/j.jag.2021.102391

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…The errors were very small, which indicated that ambient/background light had little effect on the accuracy of BPNNRCM model. [53] improved the performance of three UAV remote sensing reflectance obtention methods (MIR, ELM and ARTM) based on incident, background and environmental radiance. The average absolute error of MIR, ELM and ARTM were 0.018, 0.017 and 0.044, respectively.…”

Section: A Performance Evaluation Of Different Radiometric Correction...mentioning

confidence: 99%

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

Zhang

Li³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Radiometric correction is one of the most important preprocessing parts of Unmanned Aerial Vehicle (UAV) multispectral remote sensing data analysis and application. In this study, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. Firstly, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Secondly, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was 5 (digital number, UAV sensor height, wavelength, solar altitude angle and temperature) and the number of nodes in the hidden layer was 8, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and crossvalidation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method (ELM) and radiative transfer model. In general, the research results of this paper prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.

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Section: A Performance Evaluation Of Different Radiometric Correction...mentioning

confidence: 99%

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

Zhang

Li³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Astronomers have been able to accurately predict the position of the Sun and Earth based on time, based on the laws of their orbits [21,22]. In astronomy, the equatorial coordinate system is commonly used to describe the position of the Sun, as shown in Figure 4b.…”

Section: Incident Geometry Of the Sunmentioning

confidence: 99%

The Method of Multi-Angle Remote Sensing Observation Based on Unmanned Aerial Vehicles and the Validation of BRDF

Cao,

You,

et al. 2023

Remote Sensing

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The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties.

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“…A UAV with a RGB camera may be accurate enough for larger objects but will depends on the objects having distinctive color and weather condition being good for the best performance [85]. UAVs with multispectral or hyperspectral sensors can achieve centimeter-level or decimeter-level resolution while flying at an altitude of several hundred meters and have great potential for monitoring of plastic debris [86]. Though multi-spectral and hyperspectral remote sensing is still in its early stages, it has long-term and global potential for monitoring plastic litter, due to the broader wavelength range and differing absorption and reflectance properties of different materials at different wavelengths.…”

Section: Challenges In Plastic Detection and Future Opportunities For...mentioning

confidence: 99%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

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Comparing, validating and improving the performance of reflectance obtention method for UAV-Remote sensing

Cited by 9 publications

References 40 publications

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

The Method of Multi-Angle Remote Sensing Observation Based on Unmanned Aerial Vehicles and the Validation of BRDF

Detection of River Plastic Using UAV Sensor Data and Deep Learning

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