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Multisource Remote Sensing Data Classification With Graph Fusion Network
Du, Xingqian1; Zheng, Xiangtao2; Lu, Xiaoqiang3; Doudkin, Alexander A.4
Department光谱成像技术研究室
2021
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN01962892;15580644
Contribution Rank1
AbstractThe land cover classification has been an important task in remote sensing. With the development of various sensors technologies, carrying out classification work with multisource remote sensing (MSRS) data has shown an advantage over using a single type of data. Hyperspectral images (HSIs) are able to represent the spectral properties of land cover, which is quite common for land cover understanding. Light detection and ranging (LiDAR) images contain altitude information of the ground, which is greatly helpful with urban scene analysis. Current HSI and LiDAR fusion methods perform feature extraction and feature fusion separately, which cannot well exploit the correlation of data sources. In order to make full use of the correlation of multisource data, an unsupervised feature extraction-fusion network for HSI and LiDAR, which utilizes feature fusion to guide the feature extraction procedure, is proposed in this article. More specifically, the network takes multisource data as input and directly output the unified fused feature. A multimodal graph is constructed for feature fusion, and graph-based loss functions including Laplacian loss and t-distributed stochastic neighbor embedding (t-SNE) loss are utilized to constrain the feature extraction network. Experimental results on several data sets demonstrate the proposed network can achieve more excellent classification performance than some state-of-the-art methods. IEEE
KeywordClassification deep learning hyperspectral image (HSI) light detection and ranging (LiDAR) remote sensing
DOI10.1109/TGRS.2020.3047130
Indexed ByEI
Language英语
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20210409830330
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/94278
Collection光谱成像技术研究室
Affiliation1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China, and also with the University of Chinese Academy of Sciences, Beijing 100049, China.;
2.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China (e-mail: xiangtaoz@gmail.com);
3.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.;
4.Laboratory of System Identification, United Institute of Informatics Problems of the National Academy of Sciences of Belarus, 220012 Minsk, Belarus, and also with the Department of Computers, Belarusian State University of Informatics and Radioelectronics (BSUIR), 220012 Minsk, Belarus.
Recommended Citation
GB/T 7714
Du, Xingqian,Zheng, Xiangtao,Lu, Xiaoqiang,et al. Multisource Remote Sensing Data Classification With Graph Fusion Network[J]. IEEE Transactions on Geoscience and Remote Sensing,2021.
APA Du, Xingqian,Zheng, Xiangtao,Lu, Xiaoqiang,&Doudkin, Alexander A..(2021).Multisource Remote Sensing Data Classification With Graph Fusion Network.IEEE Transactions on Geoscience and Remote Sensing.
MLA Du, Xingqian,et al."Multisource Remote Sensing Data Classification With Graph Fusion Network".IEEE Transactions on Geoscience and Remote Sensing (2021).
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