Extraction of typical natural resource elements based on multi-source high-resolution remote sensing images
Using high-resolution remote sensing data with high spatial resolution characteristics, typical natural resource elements are extracted based on traditional convolutional neural network deep learning algorithms using multi-source high-resolution remote sensing images of 0. 3 and 1 m in Xining, Qinghai province as data sources. The results show that the accuracy of extracting farmland and forest land from 0. 3 m remote sensing images is over 85％, with a recall rate of over 89％. The accuracy of extracting farmland and forest land from 1 m remote sensing images is over 90％, with a recall rate of over 91％. The research results can be used for intelligent extraction of typical elements of natural resources in Xining.
万方数据
维普
