Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

By Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo, Guisong Xia. 2020

818 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ecology, Media Technology

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Citations in the corpus

Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “These improvements bring challenging issues to the widespread use of Remote Sensing (RS) classification [2] and semantic segmentation techniques, due to (a) the continuous arrival of new data, possibly belonging to partially overlapping domains, (b) the sheer size of the datasets, requiring vast amo”
FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image (2022, positive): “D EEP neural networks (DNNs) trained in a supervised learning manner have made remarkable progress in remote sensing image (RSI) scene classiﬁcation [1], target detection [2], and semantic segmentation [3], [4].”
Deep learning for change detection in remote sensing: a review (2022, background): “…image fusion (Shao, and Cai 2018), image registration (Wang et al. 2018b), image matching (Chen, Rottensteiner, and Heipke 2021; Zhang et al. 2020; Cheng et al. 2020), change detection (Zhu et al. 2018), land use and cover classification (Hong et al. 2021, 2020b, 2020a; Li et al. 2022; Zhou et…”
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling (2023, background): “Beneﬁting from the deep learning, many tasks using RS data have made remarkable progress [8]–[17].”
Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021, background): “INTRODUCTION Recent years have witnessed a rapid development in remote sensing (RS) technologies [1]–[3].”
Vision-Language Models for Vision Tasks: A Survey (2023, background): “Visual recognition ( e.g. , image classification, object detection and semantic segmentation) is a long-standing challenge in computer vision research, and it is also the cornerstone of a myriad of computer vision applications in autonomous driving [1], remote sensing [2], robotics [3], etc.”