OPT OpenIR  > 光学影像学习与分析中心
Remote Sensing Image Scene Classification Using Rearranged Local Features
Yuan, Yuan1; Fang, Jie2,3; Lu, Xiaoqiang1; Feng, Yachuang1
Department光学影像学习与分析中心
2019-03
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892;1558-0644
Volume57Issue:3Pages:1779–1792
Contribution Rank1
Abstract

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.

KeywordFeature fusion rearranged local features remote sensing image representation scene classification
DOI10.1109/TGRS.2018.2869101
Indexed BySCI ; EI
Language英语
WOS IDWOS:000460321300043
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20191106643944
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31321
Collection光学影像学习与分析中心
Corresponding AuthorLu, Xiaoqiang
Affiliation1.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
Recommended Citation
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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