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GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection
Wang, Qi1,2; Yuan, Zhenghang3,4; Du, Qian5; Li, Xuelong6,7
Department光学影像学习与分析中心
2019-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892;1558-0644
Volume57Issue:1Pages:3-13
Contribution Rank6
Abstract

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-to-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.

Keyword2-D convolutional neural network (CNN) change detection (CD) deep learning hyperspectral image (HSI) mixed-affinity matrix spectral unmixing
DOI10.1109/TGRS.2018.2849692
Indexed BySCI
Language英语
WOS IDWOS:000455089000001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31164
Collection光学影像学习与分析中心
Corresponding AuthorWang, Qi
Affiliation1.Northwestern Polytech Univ, Sch Comp Sci, Ctr OPT IMagery Anal & Learning, Xian 710072, Shaanxi, Peoples R China
2.Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shaanxi, Peoples R China
3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning, Xian 710072, Shaanxi, Peoples R China
5.Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA
6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Wang, Qi,Yuan, Zhenghang,Du, Qian,et al. GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(1):3-13.
APA Wang, Qi,Yuan, Zhenghang,Du, Qian,&Li, Xuelong.(2019).GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(1),3-13.
MLA Wang, Qi,et al."GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.1(2019):3-13.
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