2022/05/26 · A novel group bilinear convolutional neural network (GBCNN) model is developed to deeply extract discriminative second-order representations of ship targets.

To be specific, a novel group bilinear convolutional neural network (GBCNN) model is developed to deeply extract discriminative second-order representations of ...

This letter proposes to explore the dual-polarization. (dual-pol) SAR images for better ship classification. To be specific, a novel group bilinear ...

A novel group bilinear convolutional neural network model is developed to deeply extract discriminative second-order representations of ship targets from ...

2022/01/01 · To be specific, a novel group bilinear convolutional neural network (GBCNN) model is developed to deeply extract discriminative second-order ...

He et al., “Group bilinear CNNs for dual-polarized SAR ship classifica- tion,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1‒5, 2022. [33]. T. Zhang and X ...

This paper proposes a novel CNN method for dualpolarized SAR ship grained classification. The network employs hybrid channel feature loss which jointly ...

2023/04/18 · DPIG-Net utilizes available dual-polarization information from the Sentinel-1 SAR satellite to adaptively guide feature extraction and feature fusion.

He et al. [25] also validated the effectiveness of using polarization for SAR ship classification, and further proposed a group bilinear pooling CNN (GBCNN) [26] ...

Group Bilinear CNNs for Dual-Polarized SAR Ship Classification · Ship Classification in Medium-Resolution SAR Images via Densely Connected Triplet CNNs ...