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Remote Sensing Image Scene Classification Using Rearranged Local Features
Yuan, Yuan1; Fang, Jie2,3; Lu, Xiaoqiang1; Feng, Yachuang1
作者部门光谱成像技术研究室
2019-03
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892;1558-0644
卷号57期号:3页码:1779–1792
产权排序1
摘要

Remote sensing image scene classification is a fundamental problem, which aims to label an image with a specific semantic category automatically. Recently, deep learning methods have achieved competitive performance for remote sensing image scene classification, especially the methods based on a convolutional neural network (CNN). However, most of the existing CNN methods only use feature vectors of the last fully connected layer. They give more importance to global information and ignore local information of images. It is common that some images belong to different categories, although they own similar global features. The reason is that the category of an image may be highly related to local features, other than the global feature. To address this problem, a method based on rearranged local features is proposed in this paper. First, outputs of the last convolutional layer and the last fully connected layer are employed to depict the local and global information, respectively. After that, the remote sensing images are clustered to several collections using their global features. For each collection, local features of an image are rearranged according to their similarities with local features of the cluster center. In addition, a fusion strategy is proposed to combine global and local features for enhancing the image representation. The proposed method surpasses the state of the arts on four public and challenging data sets: UC-Merced, WHU-RS19, Sydney, and AID.

关键词Feature fusion rearranged local features remote sensing image representation scene classification
DOI10.1109/TGRS.2018.2869101
收录类别SCI ; EI
语种英语
WOS记录号WOS:000460321300043
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号20191106643944
引用统计
被引频次:83[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/31321
专题光谱成像技术研究室
通讯作者Lu, Xiaoqiang
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Yuan, Yuan,Fang, Jie,Lu, Xiaoqiang,et al. Remote Sensing Image Scene Classification Using Rearranged Local Features[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(3):1779–1792.
APA Yuan, Yuan,Fang, Jie,Lu, Xiaoqiang,&Feng, Yachuang.(2019).Remote Sensing Image Scene Classification Using Rearranged Local Features.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(3),1779–1792.
MLA Yuan, Yuan,et al."Remote Sensing Image Scene Classification Using Rearranged Local Features".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.3(2019):1779–1792.
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