关键词: 高光谱遥感图像, 贝叶斯深度学习, 主动学习, 分类

Abstract: In order to solve the problems that the acquisition of labeled samples are time-consuming and difficult for hyperspectral 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, arrcoding 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 according 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.

Key words: hyperspectral remote sensing image, Bayesian deep learning, active learning, classification