Indexed by:会议论文

Date of Publication:2016-07-10

Included Journals:EI、CPCI-S、Scopus

Volume:2016-November

Page Number:3282-3285

Key Words:Deep learning; Supervised classification; Hyperspectral image

Abstract: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.