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	An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification
	Nie, Xiangli; Yongkang Luo; Bo Zhang; Hong Qiao; Zhongping Jiang
	2018
会议日期	8.20-8.24
会议地点	Beijing
英文摘要	The fast and accurate classification of polarimetric synthetic aperture radar (PolSAR) data in dynamically changing environments is an important and challenging task. In this paper, we propose an Incremental Multi-view Passive-Aggressive Active learning algorithm, named IMPAA, for PolSAR data classification. This algorithm can deal with online two-view multi-class categorization problem by exploiting the relationship between the polarimetric-color and texture feature sets of PolSAR data. In addition, the IMPAA algorithm can handle the dynamic largescale datasets where not only the amount of data but also the number of classes gradually increases. Moreover, this algorithm only queries the class labels of some informative incoming samples to update the classifier based on the disagreement of different views’ predictors and a randomized rule. Experiments on real PolSAR data demonstrate that the proposed method can use a smaller fraction of queried labels to achieve low online classification errors compared with previously known methods.
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/40988]
专题	自动化研究所_复杂系统管理与控制国家重点实验室
推荐引用方式 GB/T 7714	Nie, Xiangli,Yongkang Luo,Bo Zhang,et al. An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification[C]. 见:. Beijing. 8.20-8.24.

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