There are numerous islands with abundant resources in China.Due to the limited information included in common polarization features and the poor effect of traditional classification methods when there are few samples，nine polarization features are analyzed and classification is carried out using active deep learning.Firstly，multiple features are extracted from an original image.Then，the original features can be extracted by anto\|encoder and the initial classifier is trained and fine-tune the whole model with a small number of labeled samples.Finally，the most uncertain samples are selected to label with active learning algorithm and added to the training samples.Experiment comfirms that active deep learning can effectively improve the classification accuracy with less labeled samples and entropy shannon is a more effective feature to distinguish between seawater，mudflats and beaches.
徐梦竹, 徐佳, 邓鸿儒, 袁春琦. 基于全极化SAR影像的海岛地物分类[J]. 遥感技术与应用, 2019, 34(3): 647-654.
Xu Mengzhu, Xu Jia, Deng Hongru, Yuan Chunqi. Land-use Classification of Islands based on Fully Polarimetric SAR Data. Remote Sensing Technology and Application, 2019, 34(3): 647-654.
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