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An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification
Nie, Xiangli1; Yongkang Luo1; Bo Zhang2; Hong Qiao1; Zhongping Jiang3
Conference NameIEEE International Conference on Pattern Recognition (ICPR2018)
Conference Date8.20-8.24
Conference PlaceBeijing
AbstractThe 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.
Indexed ByEI
Document Type会议论文
Affiliation1.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
Recommended Citation
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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