基于未标签信息主动学习算法的高光谱影像分类

发布时间：2018-07-07 18:58

  本文选题：高光谱遥感 + 主动学习　； 参考：《计算机应用》2017年06期

【摘要】：针对高光谱遥感影像分类中,传统的主动学习算法仅利用已标签数据训练样本,大量未标签数据被忽视的问题,提出一种结合未标签信息的主动学习算法。首先,通过K近邻一致性原则、前后预测一致性原则和主动学习算法信息量评估3重筛选得到预测标签可信度高并具备一定信息量的未标签样本;然后,将其预测标签当作真实标签加入到标签样本集中;最后,训练得到更优质的分类模型。实验结果表明,与被动学习算法和传统的主动学习算法相比,所提算法能够在同等标记的代价下获得更高的分类精度,同时具有更好的参数敏感性。
[Abstract]:In hyperspectral remote sensing image classification, the traditional active learning algorithm only uses labeled data to train samples and a large number of untagged data are ignored. An active learning algorithm combining untagged information is proposed. First of all, through K-nearest neighbor consistency principle, prediction consistency principle and active learning algorithm information evaluation, the untagged samples with high credibility and certain amount of information are obtained. The prediction tag is added to the tag sample set as a real tag. Finally, a better classification model is obtained by training. The experimental results show that compared with the passive learning algorithm and the traditional active learning algorithm, the proposed algorithm can achieve higher classification accuracy and better parameter sensitivity at the same cost of marking.
【作者单位】： 湖北大学资源环境学院;武汉大学遥感信息工程学院;国网湖北省电力公司检修公司;
【基金】：国家自然科学基金资助项目(41601504)~~
【分类号】：P237
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本文编号：2105941