Dual-Polarized SAR Ship Grained Classification Based on CNN With Hybrid Channel Feature Loss
Liang Zeng, Qingtao Zhu, Danwei Lu, Tao Zhang, Hongmiao Wang, Junjun Yin, Jian Yang
Abstract
This letter proposes a novel convolutional neural network (CNN) method for dual-polarized synthetic aperture radar (SAR) ship grained classification. The network employs hybrid channel feature loss that jointly utilizes the information contained in the polarized channels (VV and VH). It is demonstrated that, by adopting the proposed CNN framework and the novel loss function, the classification performance can be efficiently improved. First, instead of the prevalently used threefold or fourfold division (container ship, oil tanker, bulk carrier, and so on), the proposed method can further divide vessels into eight accurate categories. Second, this method can not only effectively classify targets into eight categories but also its accuracy in terms of fewer category classifications surpasses existing methods. Third, the method can achieve good performance on a small training data set. Experiments conducted on the OpenSARShip data sets indicate that the proposed classification method achieves state-of-the-art results.