Multisource Remote Sensing Data Classification With Graph Fusion Network

doi:10.1109/TGRS.2020.3047130

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Multisource Remote Sensing Data Classification With Graph Fusion Network
	Du, Xingqian 4; Zheng, Xiangtao 3; Lu, Xiaoqiang 2; Doudkin, Alexander A.1
刊名	IEEE Transactions on Geoscience and Remote Sensing
	2021
关键词	Classification deep learning hyperspectral image (HSI) light detection and ranging (LiDAR) remote sensing
ISSN号	01962892;15580644
DOI	10.1109/TGRS.2020.3047130
产权排序	1
英文摘要	The 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
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/94278]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.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. 2.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.; 3.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); 4.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.;
推荐引用方式 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).