In recent five years, convolutional neural network (CNN) has been fully developed. In the field of image classification, the
algorithm based on supervised learning has achieved great success in related tasks. However, compared with coarse-grained label data sets, the classification of fine-grained label data sets is still a difficult point. Geographic images are widely used in all aspects of society. Researchers often need to classify large-scale geographic image data. However, due to the small feature difference of geographic images, automatic classification is relatively difficult. In this paper, the fine-grained features of geographic images are labeled, and trained and learned through the deep convolution network, which greatly improves the classification accuracy of geographic images.
