Multilayer semantic segmentation of remote-sensing imagery using a   hybrid object-based Markov random field model

doi:10.1080/01431161.2016.1244364

CORC > 北京大学 > 地球与空间科学学院

	Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model
	Zheng, Chen ; Zhang, Yun ; Wang, Leiguang
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2016
关键词	GIBBS RANDOM-FIELDS SPATIAL CONSTRAINTS ACTIVE CONTOURS CLASSIFICATION PENALTIES
DOI	10.1080/01431161.2016.1244364
英文摘要	High spatial resolution (HR) remote-sensing image usually contains hierarchical semantic information. Many supervised methods have been developed to interpret this information through data training. In this article, without data training, a hybrid object-based Markov random field (HOMRF) model is proposed for multi-layer semantic segmentation of remote-sensing images. In this method, label fields of different semantic layers are defined on the same region adjacency graph (RAG) of a given image, and a hybrid framework is suggested to capture and utilize the interactions within and between semantic layers by label fields. Namely a new transition probability matrix is introduced into the energy functions of label fields for describing the semantic context between layers, and the multilevel logistic model is employed to describe the interactions within the same layer. A principled probabilistic inference is developed to determine the optimal solution of the proposed method by iteratively updating each label field until convergence. The computational complexity of the proposed model is O(knt), where k is the number of classes in all of the layers, n is the number of sites in the probability graph of the MRF model, and t is the number of iterations. Experimental results from various remote-sensing images demonstrate that the proposed method can produce higher segmentation accuracy than state-of-the-art MRF-based methods.; China Scholarship Council [201408410204]; Canada Research Chairs; National Natural Science Foundation of China [41301470, 41571372]; Key Technology Projects of Henan Educational Department of China [15A420001]; basic research funds for the Henan provincial universities; SCI(E); ARTICLE; zhengchen_data@126.com; 23; 5505-5532; 37
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/458339]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Zheng, Chen,Zhang, Yun,Wang, Leiguang. Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2016.
APA	Zheng, Chen,Zhang, Yun,&Wang, Leiguang.(2016).Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model.INTERNATIONAL JOURNAL OF REMOTE SENSING.
MLA	Zheng, Chen,et al."Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model".INTERNATIONAL JOURNAL OF REMOTE SENSING (2016).