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 article 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: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, 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 datasets. Finally, we discuss the promising opportunities for further research.

中文翻译：

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遥感图像场景分类遇到深度学习：挑战、方法、基准和机遇

遥感图像场景分类旨在根据遥感图像的内容用一组语义类别对其进行标记，在许多领域具有广泛的应用。在深度神经网络强大的特征学习能力的推动下，深度学习驱动的遥感图像场景分类备受关注并取得重大突破。然而，据我们所知，目前还缺乏对遥感图像场景分类深度学习的最新成果的全面回顾。考虑到该领域的快速发展，本文通过覆盖160多篇论文对遥感图像场景分类的深度学习方法进行了系统综述。再具体一点，我们讨论了遥感影像场景分类和调查的主要挑战：第一，基于自编码器的遥感影像场景分类方法；二、基于卷积神经网络的遥感影像场景分类方法；第三，基于生成对抗网络的遥感图像场景分类方法。此外，我们介绍了用于遥感图像场景分类的基准，并总结了两种以上代表性算法在三个常用基准数据集上的性能。最后，我们讨论了进一步研究的有希望的机会。基于生成对抗网络的遥感图像场景分类方法。此外，我们介绍了用于遥感图像场景分类的基准，并总结了两种以上代表性算法在三个常用基准数据集上的性能。最后，我们讨论了进一步研究的有希望的机会。基于生成对抗网络的遥感图像场景分类方法。此外，我们介绍了用于遥感图像场景分类的基准，并总结了两种以上代表性算法在三个常用基准数据集上的性能。最后，我们讨论了进一步研究的有希望的机会。