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论文类型：会议论文

发表时间：2016-07-10

收录刊物：EI、CPCI-S、Scopus

卷号：2016-November

页面范围：3282-3285

关键字：Deep learning; Supervised classification; Hyperspectral image

摘要：This paper presents a hyperspectral image classification method based on deep network, which has shown great potential in various machine learning tasks. Since the quantity of training samples is the primary restriction of the performance of classification methods, we impose a new prior on the deep network to deal with the instability of parameter estimation under this circumstances. On the one hand, the proposed method adjusts parameters of the whole network to minimize the classification error as all supervised deep learning algorithm, on the other hand, unlike others, it also minimize the discrepancy within each class and maximize the difference between different classes. The experimental results showed that the proposed method is able to achieve great performance under small training set.