An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification
Xiangli Nie, Yongkang Luo, Hong Qiao, Bo Zhang, Zhong‐Ping Jiang · 2018
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 large-scale 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.