Hyperspectral Image Classification Based on Sparse Superpixel Graph

doi:10.3390/rs13183592

	Hyperspectral Image Classification Based on Sparse Superpixel Graph
	Zhao, Yifei 1,2,3; Yan, Fengqin 2
刊名	REMOTE SENSING
	2021-09-01
卷号	13 期号:18 页码:18
关键词	hyperspectral image sparse superpixel graph spectral-spatial classification discrete potential big data
DOI	10.3390/rs13183592
通讯作者	Yan, Fengqin(yanfq@lreis.ac.cn)
英文摘要	Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.
资助项目	National Natural Science Foundation of China
WOS关键词	FEATURE-EXTRACTION ; NEURAL-NETWORK ; REPRESENTATION ; INFORMATION
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000701473900001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/166063]
专题	中国科学院地理科学与资源研究所
通讯作者	Yan, Fengqin
作者单位	1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yifei,Yan, Fengqin. Hyperspectral Image Classification Based on Sparse Superpixel Graph[J]. REMOTE SENSING,2021,13(18):18.
APA	Zhao, Yifei,&Yan, Fengqin.(2021).Hyperspectral Image Classification Based on Sparse Superpixel Graph.REMOTE SENSING,13(18),18.
MLA	Zhao, Yifei,et al."Hyperspectral Image Classification Based on Sparse Superpixel Graph".REMOTE SENSING 13.18(2021):18.