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
A Collaborative Correlation-Matching Network for Multi-modality Remote Sensing Image Classification
TL;DR: Wang et al. as mentioned in this paper designed a collaborative correlation-matching network (CCM-Net) for multimodality remote sensing image classification, which utilizes single-modality dominant features as supplementary supervision information to establish the joint optimization loss function, thereby adaptively narrowing the differences between modalities before the feature extraction.
Posted Content
Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning.
TL;DR: In this paper, a large-scale training and testing dataset for remote sensing image retrieval and classification is presented, including both vertical and oblique aerial images and an associated fine-tuning method.
Journal ArticleDOI
Multiform Ensemble Self-Supervised Learning for Few-Shot Remote Sensing Scene Classification
TL;DR: In this paper , a multiform ensemble self-supervised learning (MES2L) framework is proposed to solve the high interclass similarity problem in remote sensing scene classification.
Journal ArticleDOI
Framework for Runway's True Heading Extraction in Remote Sensing Images Based on Deep Learning and Semantic Constraints
TL;DR: In this paper , the authors proposed an automated runway heading extraction method, considering various runway surface materials and spatial structure differences encountered in wide-area detection, promoting a quick and reliable broad investigation.
Journal ArticleDOI
Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning
Ni Ruiwen,Mu Ye,Liping Ji,Zhang Tong,Luo Tianye,Feng Ruilong,Gong He,Hu Tianli,Sun Feng Yu,Guo Ying,Li Shijun,Thobela Louis Tyasi +11 more
TL;DR: In this paper , a semantic segmentation method based on U-net network multi-task learning is proposed to accurately segment architectural features in high-resolution remote sensing images, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building.
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