PDFL: Polarimetric Decomposition Feature Learning via Deep Autoencoder

Model-based polarimetric target decomposition (TD) generally solves scattering components and parameters under a preset decomposition base; then, decomposition features are also obtained. However, a preset base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs exploring additional information or considering limiting conditions to build equations, which is hard and easily brings negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this article. Strictly, this model is not an incoherent TD method but a learning-based method. It does not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to the scattering characteristics of the current dataset. Due to the characteristics of unsupervised reconstruction, the deep autoencoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts the latent vector from polarimetric synthetic aperture radar (PolSAR) data, and then, the decoder reconstructs pseudodata on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of the decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, the proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks.