协同主动学习和半监督方法的海冰图像分类

海冰遥感光谱影像分类中标签样本难以获取,导致海冰分类精度难以提高,但是大量包含丰富信息的未标签样本却没有得到充分利用,针对这种情况,提出一种协同主动学习和半监督学习方法用于海冰遥感图像分类。在主动学习部分,结合最优标号和次优标号、自组织映射神经网络以及增强的聚类多样性算法来选择兼具不确定性和差异性的样本参与训练;在半监督学习部分,利用直推式支持向量机,并且融合主动学习思想从大量未标签样本中选取相对可靠且包含一定信息量的样本进行迭代训练;然后协同主动学习分类结果和半监督分类结果,通过一致性验证保证所加入伪标签样本的正确性。为了验证方法的有效性,分别采用巴芬湾地区30 m分辨率的Hyperion高光谱数据(验证数据为15 m分辨率的Landsat-8数据)和辽东湾地区15 m分辨率的Landsat-8数据(验证数据为4.77 m分辨率的Google Earth数据)进行海冰分类实验。实验结果表明,相对其他传统方法,该协同分类方法可以在只有少量标签样本的情况下,充分利用大量未标签样本中包含的信息,实现快速收敛,并获得较高的分类精度(两个实验的总体精度分别为90.003%和93.288%),适用于海冰遥感图像分类。

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

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.