  Remote sensing image scene classification, which aims at labeling remote
sensing 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 paper provides a systematic survey of deep
learning methods for remote sensing image scene classification by covering more
than 160 papers. To be specific, we discuss the main challenges of remote
sensing image scene classification and survey (1) Autoencoder-based remote
sensing image scene classification methods, (2) Convolutional Neural
Network-based remote sensing image scene classification methods, and (3)
Generative Adversarial Network-based remote sensing image scene classification
methods. In addition, we introduce the benchmarks used for remote sensing image
scene classification and summarize the performance of more than two dozen of
representative algorithms on three commonly-used benchmark data sets. Finally,
we discuss the promising opportunities for further research.
