计算机工程与应用2019，Vol.55Issue(18)：166-172,7.DOI:10.3778/j.issn.1002-8331.1805-0427
基于深度贝叶斯主动学习的高光谱图像分类
Active Learning for Hyperspectral Image Classification with Deep Bayesian
摘要
Abstract
In order to solve the problems that the acquisition of labeled samples are time-consuming and difficult for hyper-spectral remote sensing images classification, unlabeled samples have not been used efficiently, and active learning cannot combine well with deep learning. Bayesian active learning hyperspectral image classification algorithm is proposed, arrcod-ing to the latest advances in Bayesian deep learning and active learning. A convolutional neural network model is trained with a small number of labeled samples, and the most uncertain samples are selected from the unlabeled samples accord-ing to the active learning sampling strategy combined with the Bayesian method. The selected samples are added to the training set to update the model. Then model uncertainty will be reduced and the model classification effect will be improved. The experimental results of PaviaU hyperspectral image classification show that using a small number of labeled samples, the proposed method performs better than the traditional method.关键词
高光谱遥感图像/贝叶斯深度学习/主动学习/分类Key words
hyperspectral remote sensing image/Bayesian deep learning/active learning/classification分类
信息技术与安全科学引用本文复制引用
杨承文,李吉明,杨东勇..基于深度贝叶斯主动学习的高光谱图像分类[J].计算机工程与应用,2019,55(18):166-172,7.基金项目
公安部科技强警基础工作专项(No.2016ABJC44). （No.2016ABJC44）
