论文标题
遥感图像场景分类符合深度学习：挑战，方法，基准和机会
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
论文作者
论文摘要
遥感图像场景分类旨在根据其内容使用一组语义类别标记遥感图像，并在一系列字段中具有广泛的应用。受深度神经网络的强大功能学习能力的推动，由深度学习驱动的遥感图像场景分类引起了极大的关注并取得了重大突破。但是，据我们所知，仍然缺乏对有关深度学习遥感图像的深度学习成就的全面回顾。考虑到该领域的快速发展，本文通过覆盖160多篇论文，对深度学习图像场景分类进行了系统的调查。要具体而言，我们讨论了遥感图像场景分类和调查的主要挑战（1）基于自动编码器的遥感图像场景分类方法，（2）基于卷积的神经网络基于近距离神经网络的遥感图像场景分类方法以及（3）生成基于对逆网络的遥控网络遥感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，并总结了三个常用的基准数据集上的二十多个代表性算法的性能。最后，我们讨论了进一步研究的有希望的机会。
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.
