Description

Most of the classification methods of urban functional areas nowadays are only based on single source data analysis and modeling, which cannot make full use of the multi scale and multi-source data that is easy to obtain, which aims at labelling urban functional areas images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this process provides a systematic survey of deep learning methods for remote sensing image scene classification. The remote sensing images are taken as the input and analyze the remote sensing scene through deep learning technique and analyzing the sensing scene of the image and the deep learning algorithm is implemented and it will train all images and predict the result based on accuracy, precision, recall and F1-score.