A DBN-crf for spectral-spatial classification of hyperspectral data

Abstract

This work shows how to improve hyperspectral image classification through using both a deep representation and contextual information. To implement this objective, this work proposes a new Conditional Random Field (CRF) model (named DBN-CRF) with potentials defined over deep features produced by the Deep Belief Networks (DBNs). The newly formulated DBN-CRF model takes advantage of strength of the DBNs in learning a good representation and the ability of CRFs to model contextual (spatial) information in both observations and labels. Within a piecewise training framework, an efficient training method is proposed to train the whole DBN-CRF model end-to-end. This means that parameters in DBN and CRF can be jointly trained and thus the proposed method can fully use the strength of both DBN and CRF. Moreover, in the proposed training method, the end-to-end training can be implemented with a standard back-propagation algorithm, avoiding the repeated inference usually involved in CRF training and thus is computationally efficient. Experiments on real-world hyperspectral data show that our method outperforms the most recent approaches in hyperspectral image classification.