An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification
Nie, Xiangli1; Yongkang Luo1; Bo Zhang2; Hong Qiao1; Zhongping Jiang3
2018
会议名称IEEE International Conference on Pattern Recognition (ICPR2018)
会议日期8.20-8.24
会议地点Beijing
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.
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/21714
专题复杂系统管理与控制国家重点实验室_复杂系统研究
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.Academy of Mathematics and Systems Science, Chinese Academy of Sciences
3.Tandon School of Engineering, New York University
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],2018.
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