Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

doi:10.1007/s00530-015-0455-8

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 高性能计算应用研究中心

	Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine
	Liu, Jun 1; Zhou, Xiran 2; Huang, Junyi 3; Liu, Shuguang4 ; Li, Huali 5; Wen, Shan 6; Liu, Junchen 7
刊名	MULTIMEDIA SYSTEMS
	2017-02-01
卷号	23 期号:1 页码:95-104
关键词	Semantic classification Hyperspectral image Relevance vector machine Multi-distance learning with multiple dimensions
ISSN号	0942-4962
DOI	10.1007/s00530-015-0455-8
通讯作者	Liu, SG (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China.
英文摘要	Accurate hyperspectral image classification requires not only image features but also semantic concept. Similarity and relevance relation are both key factors in building image features and semantic measurement. To perform hyperspectral image classification from the viewpoint of semantic, this study focuses on creating a semantic annotation-based image classification method with relevance and similarity measurement. First, the computational model of relevance vector machine is utilized to perform cluster computation for hyperspectral image data. Then multi-distance learning algorithm is optimized as holding capability for multiple dimensions data. The proposed multi-distance learning algorithm with multiple dimensions is used to measure the similarity, according to the result of cluster computation through relevance vector machine. Finally, semantic annotation is introduced to complete classification of hyperspectral image with semantic concept. Validation with the ground truth data illustrates that the proposed method can provide more accurate and integrated classification result compared with the other methodologies. Therefore, the integration of similarity and relevance measurement is able to improve the performance of hyperspectral image classification.
资助项目	International Science and Technology Collaboration Project of China[2010DFA92720-24] ; National Natural Science Foundation program[41301403] ; National Natural Science Foundation program[41471340] ; Chongqing Basic and Advanced Research General Project[cstc2013jcyjA40010] ; Hunan Provincial Natural Science Foundation of China[S2013J504B]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000393759100010
内容类型	期刊论文
源URL	[http://172.16.51.4:88/handle/2HOD01W0/259]
专题	高性能计算应用研究中心
通讯作者	Liu, Shuguang
作者单位	1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China 2.Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA 3.Hong Kong Baptist Univ, Dept Geog, Hong Kong, Hong Kong, Peoples R China 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 5.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China 6.Yunnan Elect Comp Ctr, Kunming 650223, Yunnan, Peoples R China 7.Tianjin Inst Surveying & Mapping, Tianjin 300381, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jun,Zhou, Xiran,Huang, Junyi,et al. Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine[J]. MULTIMEDIA SYSTEMS,2017,23(1):95-104.
APA	Liu, Jun.,Zhou, Xiran.,Huang, Junyi.,Liu, Shuguang.,Li, Huali.,...&Liu, Junchen.(2017).Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine.MULTIMEDIA SYSTEMS,23(1),95-104.
MLA	Liu, Jun,et al."Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine".MULTIMEDIA SYSTEMS 23.1(2017):95-104.