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
Subject
General Earth and Planetary Sciences
Reference23 articles.
1. An NSST-Based Fusion Method for Airborne Dual-Frequency, High-Spatial-Resolution SAR Images;Huang;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2023
2. Modified Adaptive 2-D Calibration Algorithm for Airborne Multichannel SAR-GMTI;Ge;IEEE Geosci. Remote Sens. Lett.,2023
3. Local Road Area Extraction in CSAR Imagery Exploiting Improved Curvilinear Structure Detector;Luo;IEEE Trans. Geosci. Remote Sens.,2022
4. Hu, X., Xie, H., Zhang, L., Hu, J., He, J., Yi, S., Jiang, H., and Xie, K. (2023). Fast Factorized Backprojection Algorithm in Orthogonal Elliptical Coordinate System for Ocean Scenes Imaging Using Geosynchronous Spaceborne–Airborne VHF UWB Bistatic SAR. Remote Sens., 15.
5. Jiang, X., Xie, H., Chen, J., Zhang, J., Wang, G., and Xie, K. (2023). Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images. Remote Sens., 15.
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