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Attention based network for remote sensing scene classification
Liu, Shaoteng1; Wang, Qi1,2; Li, Xuelong3,4
2018-10-31
Conference Name38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Source Publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Volume2018-July
Pages4740-4743
Conference Date2018-07-22
Conference PlaceValencia, Spain
PublisherInstitute of Electrical and Electronics Engineers Inc.
Contribution Rank3
AbstractScene classification of very high resolution remote sensing images is becoming more and more important because of its wide range of applications. However, previous works are mainly based on handcrafted features which do not have enough adaptability and expression ability. In this paper, inspired by the attention mechanism of human visual system, we propose a novel attention based network (AttNet) for scene classification. It can focus selectively on some key areas of images so that it can abandon redundant information. Essentially, AttNet gives a way to readjust the signal of supervision, and it is one of the first successful attempts on visual attention for remote sensing scene classification. Our method is evaluated on the UC Merced Land-Use Dataset, in comparison with some state-of-the-art methods. The experimental result shows that the proposed method makes a great improvement on both convergence speed and classification accuracy, and it also shows the effectiveness of visual attention for this task. © 2018 IEEE.
Department光学影像学习与分析中心
DOI10.1109/IGARSS.2018.8519232
Indexed ByEI
ISBN9781538671504
Language英语
EI Accession Number20191606775079
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31388
Collection光学影像学习与分析中心
Affiliation1.School of Computer Science, Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China;
2.Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China;
3.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China;
4.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
Liu, Shaoteng,Wang, Qi,Li, Xuelong. Attention based network for remote sensing scene classification[C]:Institute of Electrical and Electronics Engineers Inc.,2018:4740-4743.
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