Cooperative active learning and semi-supervised method for sea ice image classification

Han Yanling; Zhao Yao; Zhou Ruyan; Zhang Yun; Wang Jing; Yang Shuhu; Hong Zhonghua

doi:10.3969/j.issn.0253-4193.2020.01.013

Volume 42 Issue 1

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Han Yanling，Zhao Yao，Zhou Ruyan, et al. Cooperative active learning and semi-supervised method for sea ice image classification[J]. Haiyang Xuebao，2020, 42(1)：123–135，doi:10.3969/j.issn.0253−4193.2020.01.013

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Cooperative active learning and semi-supervised method for sea ice image classification

doi: 10.3969/j.issn.0253-4193.2020.01.013

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2018-12-17
Rev Recd Date: 2019-05-10

Available Online: 2021-04-21

Publish Date: 2020-01-25

Abstract

Abstract

In the classification of sea ice remote sensing spectral images, labeled samples are obtained difficultly, which leads to difficulty improving the accuracy of sea ice classification. But a large number of unlabeled samples containing abundant information are not fully utilized. In view of this situation, a method combining active learning and semi-supervised learning is proposed to study the classification of sea ice remote sensing spectral images. The active learning part combines BVSB, SOM neural networks and ECBD algorithm to select representative samples containing uncertainty and diversity for training. The semi-supervised learning part integrates the idea of active learning use TSVM to select relatively reliable samples containing information from a large number of unlabeled samples for iterative training. Then, the results of classification and semi supervised classification are used cooperatively to guarantee the correctness of the pseudo-labeled samples through consistency verification. To verify the effectiveness of the method, Hyperion hyperspectral data with a resolution of 30 m in Baffin Bay area (the verification data is Landsat-8 data with a resolution of 15 m) and Landsat-8 data with a resolution of 15 m in Liaodong Bay (the verification data is Google Earth data with a resolution of 4.77 m) area are used for experiments of sea ice classification. The experimental results show that the cooperative classification method can make full use of the information contained in a large number of unlabeled samples in the case of a small number of label samples, and achieve rapid convergence higher classification accuracy (the overall accuracy is 90.003% and 93.288%, respectively), which verifies that the method is suitable for classification of sea ice remote sensing.
- sea ice classification,
- active learning,
- semi-supervised learning,
- transductive support vector machine,
- cooperative training

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References(20)

References

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