Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

doi:10.3390/rs12091528

	Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method
	Zhao, Yifei 1,2,3; Su, Fenzhen 1,2; Yan, Fengqin 1,2
刊名	REMOTE SENSING
	2020-05-01
卷号	12 期号:9 页码:20
关键词	hyperspectral image superpixel weighted connectivity graph discrete potential semi-supervised classification
DOI	10.3390/rs12091528
通讯作者	Su, Fenzhen(sufz@lreis.ac.cn)
英文摘要	Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu-Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral-spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.
资助项目	National Natural Science Foundation of China[41890854]
WOS关键词	FEATURE-EXTRACTION ; SEGMENTATION ; ALGORITHM ; SUPPORT ; OCEAN
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000543394000175
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/311030/162362]
专题	中国科学院地理科学与资源研究所
通讯作者	Su, Fenzhen
作者单位	1.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, 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.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yifei,Su, Fenzhen,Yan, Fengqin. Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method[J]. REMOTE SENSING,2020,12(9):20.
APA	Zhao, Yifei,Su, Fenzhen,&Yan, Fengqin.(2020).Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method.REMOTE SENSING,12(9),20.
MLA	Zhao, Yifei,et al."Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method".REMOTE SENSING 12.9(2020):20.