Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features

Feature extraction and image classification using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in SAR applications. Generally, PolSAR image classification is a high-dimensional nonlinear mapping problem. Sparse representation-based techniques have shown great potential for pattern recognition problems. Therefore, on the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper. First, the effective features are extracted to describe the distinction of each class. Then, the feature vectors of the training samples construct an over-complete dictionary and obtain the corresponding sparse coefficients; meanwhile, the residual error of the pending pixel with respect to each atom is evaluated and considered as the criteria for classification, and the ultimate class results can be obtained according to the atoms with the least residual error. In addition, a Simplified Matching Pursuit (SMP) algorithm is proposed to solve the optimization problem of sparse representation of PolSAR images. The verification tests are implemented using Danish EMISAR L-band fully polarimetric SAR data of Foulum area, Denmark. The preliminary experimental results confirm that the proposed method outputs an excellent result and moreover the classification process is simpler and less time consuming.