UAV remote sensing image has the characteristics of higher spatial resolution, fine timeliness and high flexibility. It is widely used in many fields such as agriculture, forestry, soil resources and so on. Especially in agriculture, it plays an important role in information acquisition of agricultural production and agricultural condition monitoring. In the experiment, high-definition digital camera and multi-spectral camera are used to capture the visible light and near-infrared remote sensing image. Two pieces of 3m*3m calibration cloth is used for radiometric calibration and SIFT algorithm was used for image mosaic. This paper discusses the application of remote sensing image in agriculture, including rice lodging monitoring, diseases and pests monitoring, crop growth monitoring and crop nutrient diagnosis. The results show that UAV remote sensing provides an effective means for agricultural condition information acquisition, and has wide application prospect.
UAV remote sensing, as a new method of remote sensing, has the characteristics of higher spatial resolution, fine timeliness and high flexibility. It is widely used in the field of natural disaster monitoring, urban planning, resource investigation, and has become one of the indispensable method of remote sensing data acquisition. However, because the UAV remote sensing platform is limited by the flight height and focal length of camera, the acquired image size is smaller, single image can't cover the entire target area. Therefore, image mosaic has become a key technology to solve the problem. Image matching and image fusion are the key techniques of image mosaic. Due to the good robustness of image scaling, translation and rotation, this paper uses the SIFT algorithm to realize image matching of UAV. Since the feature extraction may produce false matches, RANSAC algorithm is applied to the feature point purification points. According to the seam-line in jointing overlap region, weighted fusion algorithm is applied to realize the image seamless splicing.
This study used a spectral index method and an artificial intelligence algorithm to quantitatively analyze rice canopy soil and plant analyzer development (SPAD) based on ground nonimaging spectral data and UAV hyperspectral images to build a high-precision SPAD prediction model for nondestructive monitoring of the chlorophyll relative content of rice in cold regions. First, this study First, this study selected characteristic bands sensitive to SPAD using uninformative variable elimination and the successive projections algorithm. Then, the correlation between commonly used vegetation indices and SPAD was analyzed. Finally, this study constructed a back propagation neural network (BPNN) model, BPNN with particle swarm optimization (PSO-BPNN) model, and BPNN with genetic algorithm optimization (GA-BPNN) model, and then verified the reliability of these models. According to the results, GA-BPNN had the best predictive effect. The coefficient of the determination reached 0.818, and the root mean square error was 0.847. GA-BPNN model combined with UAV hyperspectral images were used for inversion mapping; the predicted range of SPAD was 33.1-41.2, which is in good agreement with the measured value (32.7-40.6). The inversion of regional rice canopy SPAD by nonimaging spectral data and UAV hyperspectral images had high credibility, which provided technical support for the scientific management of rice in a cold region.
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