Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
TLDR
This article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers and discusses the main challenges of remote sensing images classification and survey.Abstract:
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.read more
Citations
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Journal ArticleDOI
Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention
TL;DR: Wang et al. as discussed by the authors proposed a deep attention convolutional neural network (CNN) for scene classification in remote sensing, which computes a new feature map as a weighted average of these original feature maps.
Journal ArticleDOI
Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
TL;DR: A bibliometric analysis of the existing works for remote sensing interpretation related to few-shot learning can be found in this article, where the authors provide a reference for scholars working on few-shotted learning research in the remote sensing field.
Journal ArticleDOI
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Lefei Zhang,Liangpei Zhang +1 more
TL;DR: This work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security.
Journal ArticleDOI
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search
TL;DR: In this article, a framework for scene classification network architecture search based on multi-objective neural evolution (SceneNet) is proposed, and the effectiveness of SceneNet is demonstrated by experimental comparisons with several deep neural networks designed by human experts.
Journal ArticleDOI
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Yang Long,Gui-Song Xia,Shengyang Li,Wen Yang,Michael Ying Yang,Xiao Xiang Zhu,Liangpei Zhang,Deren Li +7 more
TL;DR: The current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations are analyzed and the general guidances on creating benchmark datasets in efficient manners are presented.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.