Research on Surface Information Extraction Based on Deep Learning and Transfer Learning

The land cover types in South China are varied, and the terrain is undulating, and the area of different land types is small, and the remote sensing monitoring work was difficult. In order to solve these problems, an automatic classification method based on transfer learning and convolutional neural network model was established in this paper, with a total classification accuracy of 98.1611%. This paper proposes a land use classification remote sensing method based on deep learning, which improved the automation level and monitoring accuracy of complex land surface remote sensing monitoring in South China, and it provided technical support for the land consolidation work in China.