Group Bilinear CNNs for Dual-Polarized SAR Ship Classification
Jinglu He, Wenlong Chang, Fuping Wang, Ying Liu, Yinghua Wang, Hongwei Liu, Yinghua Li, Lei Liu
Abstract
Ship classification from synthetic aperture radar (SAR) images tends to be a hotspot in the remote sensing community. Currently, more efforts have been made to the single-polarization (single-pol) SAR ship classification with limited performance. This letter proposes to explore the dual-polarization (dual-pol) SAR images for better ship classification. To be specific, a novel group bilinear convolutional neural network (GBCNN) model is developed to deeply extract discriminative second-order representations of ship targets from the pairwise VH and VV polarization SAR images. Particularly, the deep bilinear features are efficiently acquired by performing the bilinear pooling on sub-groups of deep feature maps derived, respectively, from the single-pol SAR images (self-bilinear pooling) and dual-pol SAR images (cross-bilinear pooling). To fully explore the polarization information, the multi-polarization fusion loss (MPFL) is constructed to train the proposed model for superior SAR ship representation learning. By extensive experiments, the proposed method can achieve an overall accuracy of 88.80% and 66.90% on the 3- and 5-category dual-pol OpenSARShip data sets, which outperform the state-of-the-art methods by at least 2.00% and 2.37%, respectively.