A Multi-source Remote Sensing Data Classification Method Based On the Classification Sample Points Extracted By the UAV

摘要

#$%^&*AU2020101054A420200730.pdf#####Abstract of Descriptions The invention discloses a multi-source remote sensing data classification method based on the classification sample points extracted by the UAV, which comprises: extract the classification sample points uniformly from aerial photographs of the UAV, and prepare sample points for calibration; obtain the classification remote sensing data sets, perform image processing on the remote sensing data sets, and perform geospatial location on the classification sample points according to the classification remote sensing image data sets; the classification remote sensing data sets include: microwave data Sentinel-i data set, multispectral Sentinel-2 data set, vegetation index data set based on Sentinel-2 data set and digital elevation model data set; through the classification sample points located by geospatial information, and use the random forest classification model to obtain the classification results. The random forest classification method of multi-source remote sensing data based on the classification sample points extracted by the UAV in the invention can realize the surface type classification mapping process quickly, effectively and cheaply; after eliminating the effects of edge classification sample points, the classification accuracy is significantly improved, especially the accuracy of the Kappa coefficient is better.Drawings of Descriptions _4 NA SWoodland orshrub Clearingorbareland Road - construction cr c Figure 1 1/9