关键词: 卷积神经网络（CNN）, 伪标签, 半监督分类, 高光谱影像

Abstract: In view of the problem of needing a large number of training samples for the depth network, this paper proposes a classification method that deep networks are effectively trained by limited labeled data. The homogeneous region increases the number of training samples. The initial classifier is trained by the increased training samples and predicts the initial category of all unknown pixels. Each initial category is aggregated into the appropriate cluster and cluster labels as a pseudo label. Hyperspectral data together with their pseudo labels are used to pre-train a deep convolutional neural network. Finally, the pre-trained deep convolution network is fine-tuned by the limited training samples added by homogenous regions. Experimental results with real hyperspectral image data sets demonstrate that the new method can successfully train deep convolutional networks with a small number of actual labeled samples and efficiently classify hyperspectral data.

Key words: Convolutional Neural Network（CNN）, pseudo labels, semi-supervised classification, hyperspectral imagery