Two-Level Feature-Fusion Ship Recognition Strategy Combining HOG Features with Dual-Polarized Data in SAR Images

Author:

Xie Hongtu1,He Jinfeng1,Lu Zheng2,Hu Jun1

Affiliation:

1. School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China

2. Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China

Abstract

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Funder

Guangdong Basic and Applied Basic Research Foundation

Shenzhen Science and Technology Program

Beijing Nova Program

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Moving Target Shadow Detection Method Based on Improved ViBe in VideoSAR Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. Transfer Adaptation Learning for Target Recognition in SAR Images: A Survey;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

3. Dual Branch Deep Network for Ship Classification of Dual-Polarized SAR Images;IEEE Transactions on Geoscience and Remote Sensing;2024

4. High-Precision Imaging and Dense Vehicle Target Detection Method for Airborne Single-Pass Circular Synthetic Aperture Radar;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. Energy-Efficient and High-Performance Ship Classification Strategy Based on Siamese Spiking Neural Network in Dual-Polarized SAR Images;Remote Sensing;2023-10-14

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