A Diversified Deep Ensemble for Hyperspectral Image Classification

Recently, deep models have shown their superiority on the representation of the hyperspectral image. However, the limited number of training samples in hyperspectral image tasks makes it difficult to obtain well-trained deep model. Generally, deep belief network (DBN) which makes use of unsupervised learning can be introduced to partly solve the problem. To further improve the above-mentioned problem, this work feds DBN into deep ensemble. However, traditional deep ensemble usually produces models that tend to be very similar to the MAP solution and each other. To overcome this problem, this work introduces a novel strategy which divides the training samples into several subsets and thus the obtained multiple models would be diversified. Then a special information fusion method is proposed to obtain the final inference. Experiments are conducted over Pavia Unversity dataset to evaluate the effectiveness of the proposed method.