Object recognition in remote sensing images using sparse deep belief networks

Object recognition has been one of the hottest issues in the field of remote sensing image analysis. In this letter, a new pixel-wise learning method based on deep belief networks (DBNs) for object recognition is proposed. The method is divided into two stages, the unsupervised pre-training stage and the supervised fine-tuning stage. Given a training set of images, a pixel-wise unsupervised feature learning algorithm is utilized to train a mixed structural sparse restricted Boltzmann machine (RBM). After that, the outputs of this RBM are put into the next RBM as inputs. By stacking several layers of RBM, the deep generative model of DBNs is built. At the fine-tuning stage, a supervised layer is attached to the top of the DBN and labels of the data are put into this layer. The whole network is then trained using the back-propagation (BP) algorithm with sparse penalty. Finally, the deep model generates good joint distribution of images and their labels. Comparative experiments are conducted on our dataset acquired by QuickBird with 60 cm resolution and the recognition results demonstrate the accuracy and efficiency of our proposed method.