Low-Altitude Photogrammetry and Remote Sensing in UAV for Improving Mapping Accuracy

Ma, Shuying; Zhang, Kai

doi:10.1155/2022/5809991

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The camera parameters and POS parameters corresponding to each aerial image are obtained according to the calibration results of the UAV camera, the airborne Differential Global Positioning System (DGPS), and the flight controller. Parameters include 178 horizontal and vertical errors less than 0.1 m [35]. Pix4D mapper software (version 4.2.27, Pix4D SA, Switzerland) was used to stitch the complete raw images generated for georeferenced spectral reflectance and VIs mosaic calibration.…”

Section: Uav Image Data Acquisitionmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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Background Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features. Results In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes. Conclusions In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

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Section: Uav Image Data Acquisitionmentioning

confidence: 99%

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

et al. 2023

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Dual-Loop Control Based on Tube-Based MPC for UAVs with Disturbance

Hong,

Chen,

Han

et al. 2023

Communications in Computer and Information Science

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Farmland boundary extraction based on the AttMobile-DeeplabV3+ network and least squares fitting of straight lines

Lu,

Wang,

et al. 2023

Front. Plant Sci.

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The rapid extraction of farmland boundaries is key to implementing autonomous operation of agricultural machinery. This study addresses the issue of incomplete farmland boundary segmentation in existing methods, proposing a method for obtaining farmland boundaries based on unmanned aerial vehicle (UAV) remote sensing images. The method is divided into two steps: boundary image acquisition and boundary line fitting. To acquire the boundary image, an improved semantic segmentation network, AttMobile-DeeplabV3+, is designed. Subsequently, a boundary tracing function is used to track the boundaries of the binary image. Lastly, the least squares method is used to obtain the fitted boundary line. The paper validates the method through experiments on both crop-covered and non-crop-covered farmland. Experimental results show that on crop-covered and non-crop-covered farmland, the network’s intersection over union (IoU) is 93.25% and 93.14%, respectively; the pixel accuracy (PA) for crop-covered farmland is 96.62%. The average vertical error and average angular error of the extracted boundary line are 0.039 and 1.473°, respectively. This research provides substantial and accurate data support, offering technical assistance for the positioning and path planning of autonomous agricultural machinery.

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Low-Altitude Photogrammetry and Remote Sensing in UAV for Improving Mapping Accuracy

Cited by 3 publications

References 20 publications

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Dual-Loop Control Based on Tube-Based MPC for UAVs with Disturbance

Farmland boundary extraction based on the AttMobile-DeeplabV3+ network and least squares fitting of straight lines

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