Abstract
In order to verify the superiority and effectiveness of extracting rice information based on UAV images. This paper takes the rice plot as the research object, and uses the portable UAV Mavic Pro for aerial photography. Preprocess the acquired UAV images to generate orthophotos with a resolution of 3.95cm/pix. Using object-oriented thinking, visual evaluation and ESP tools are combined to quickly select the optimal segmentation scale to be 300, and support is applied. Vector machine, random forest, and nearest neighbor supervised classification methods have carried out ground object classification and rapid extraction of rice area. The classification results and area accuracy are evaluated by visual classification results. The method with the highest overall accuracy is the nearest neighbor classification method. At this time, the user accuracy of rice classification is 95%, and the area consistency accuracy is 99%. The results show that UAV remote sensing and automatic classification can quickly obtain high resolution images and extract rice planting area in plain rice planting area, make up for the lack of ground survey data when Nongshan is blocked, and provide samples and verification basis for the calculation of large-scale rice planting area, yield and other information.
Highlights
Aerospace remote sensing monitors a very large scale, but it contains less information due to its low spatial resolution
The results show that the application of low-altitude remote sensing technology for unmanned aerial vehicles and satellite remote sensing images is feasible in the extraction of South African ginseng and other varieties of planting areas, and it provides a scientific reference for the poverty alleviation policy of traditional Chinese medicine
In order to verify the superiority and effectiveness of extracting rice information based on UAV images, two methods are used to analyze the results, one is to use the calculation confusion matrix to evaluate the classification accuracy, and the other is to compare the extracted rice area with Compare the actual rice area and evaluate the area
Summary
Aerospace remote sensing monitors a very large scale, but it contains less information due to its low spatial resolution. Terrestrial remote sensing has high spatial resolution to obtain rich information, but the monitoring range is limited. With the rapid development of microcomputers, communication equipment and other technologies, the technology of drones equipped with remote sensing has the characteristics of flexible flight, wide monitoring range, and rich data acquisition [2]. Interpret the picture with color feature, texture feature and shape feature, select the appropriate classification method, construct the accurate extraction and measurement of crop area, provide a new method for rapid extraction of planting area, and meet the strategic requirements of the national precision agriculture development [3]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
