通信学报2018，Vol.39Issue(4)：91-99,9.DOI:10.11959/j.issn.1000-436x.2018067
主动学习策略融合算法在高光谱图像分类中的应用
Combination strategy of active learning for hyperspectral images classification
摘要
Abstract
In order to improve the phenomena of jitter and instability of the traditional active learning single strategy al-gorithm in selecting the most valuable unlabeled samples. The idea of weighted combination of ensemble learning classi-fier and proposes a joint selection based on the combination strategy method (ESAL, ensemble strategy active learning) was introduced, the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability. By analyzing the classification results of hyperspectral remote sensing images, the ESAL algorithm can save 25.4% of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67% when the same accuracy threshold is obtained, and the jitter is obvi-ously improved. ESAL algorithm is out of good stability.关键词
主动学习/集成学习/高光谱图像/策略组合Key words
active learning/ensemble learning/hyperspectral image/strategy combination分类
信息技术与安全科学引用本文复制引用
崔颖,徐凯,陆忠军,刘述彬,王立国..主动学习策略融合算法在高光谱图像分类中的应用[J].通信学报,2018,39(4):91-99,9.基金项目
国家自然科学基金资助项目(No.61675051) （No.61675051）
教育部博士点基金资助项目(No.20132304110007)The National Natural Science Foundation of China(No.61675051),Education Ministry Doctoral Research Foundation of China(No.20132304110007) （No.20132304110007）
