Dual-Branch CNN Incorporating Multiscale SVD Profile for PolSAR Image Classification

Convolutional neural networks (CNNs) have become a popular and powerful tool for polarimetric synthetic aperture radar (PolSAR) image classification. The success of the CNN model is dependent on the features that the networks extract from the polarimetric channels during the learning phase. To extract better discriminative features, we propose a novel two-step method. In the first step, by exploiting singular value decomposition (SVD), a multiscale SVD profile (MSVDP) is constructed that models spatial information of each pixel of the PolSAR image in multiple scales. In the second step, a lightweight and shallow dual-branch CNN is proposed to take the original PolSAR image and the constructed MSVDP as inputs for extracting more discriminative features during the learning of the CNN model. The effectiveness of the proposed model is validated using three real PolSAR datasets. Our proposed technique provides accurate and satisfactory results irrespective of the considered polarimetric feature sets and power descriptors. Source code for the dual-branch CNN is available at https://github.com/ ND-PatternHunter/DB-SVD-CNN