POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information

• Published in: IEEE transactions on geoscience and remote sensing 2016-06, Vol.54 (6), p.3292-3308
• Main Authors: Liu, Fang, Jiao, Licheng, Hou, Biao, Yang, Shuyuan
• Format: Article
• Language: English
• Subjects: Cleaning; Covariance matrices; Deep belief network (DBN); Feature extraction; Gaussian distribution; local spatial information; Neural networks; polarimetric synthetic aperture radar (POL-SAR); restricted Boltzmann machine (RBM); Scattering; Support vector machines; Synthetic aperture radar; Wishart-Bernoulli RBM (WBRBM)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2016.2514504

Inspired by a popular deep neural network, i.e., deep belief network (DBN), a novel method for polarimetric synthetic aperture radar (POL-SAR) image classification is proposed in this paper. For the particularity of POL-SAR data, a new type of restricted Boltzmann machine (RBM) is specially defined, which we name the Wishart-Bernoulli RBM (WBRBM), and is used to form a deep network named as Wishart DBN (W-DBN). Numerous unlabeled POL-SAR pixels are made full use of in the modeling of POL-SAR pixels by W-DBN. In addition, the coherency matrix is used directly to represent a POL-SAR pixel without any manual feature extraction, which is simple and time saving. Local spatial information, together with the confusion matrix, is used in this paper to clean the preliminary classification result obtained by the method based on W-DBN. Making full use of the prior knowledge of POL-SAR data and local spatial information, the proposed method overcomes shortcomings of traditional methods, in which they are sensitive to extracted features and slow to execute. The experiments, tested on three POL-SAR data sets, show that the proposed method produces better results and is much faster than traditional methods.