中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine
	Liu, Jun1; Zhou, Xiran2; Huang, Junyi3; Liu, Shuguang4; Li, Huali5; Wen, Shan6; Liu, Junchen7
	2017-02-01
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.
关键词	Semantic classification Hyperspectral image Relevance vector machine Multi-distance learning with multiple dimensions
DOI	10.1007/s00530-015-0455-8
发表期刊	MULTIMEDIA SYSTEMS
ISSN	0942-4962
卷号	23期号:1页码:95-104
通讯作者	Liu, SG (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China.
收录类别	SCI
WOS记录号	WOS:000393759100010
语种	英语