Dual-Polarization SAR Ship Target Recognition Based on Mini Hourglass Region Extraction and Dual-Channel Efficient Fusion Network

A novel Dual-polarization SAR ship target recognition method based on feature and loss fusion deep network is proposed in this paper, to improve the generalization ability, the recognition accuracy of imbalance samples, and the real-time performance of the general deep learning network. The proposed combined network is composed of two parts. The first part is the target region extraction network based on lightweight mini Hourglass network, to eliminate the impact of data imbalance and background noise on the identification accuracy. The second part is a two-channel feature/loss function fusion network based on Efficient B2 backbone network, aiming to solve the problem of dual-polarization image feature fusion and iterative convergence acceleration. The proposed method is tested on SAR image ship slices from the OpenSAR data sets. The experimental results indicates that, the proposed method achieves a recognition rate of 110FPS with recognition accuracy of 87.72%, and exceeds the SOTA by recognition accuracy 3.72% with convergence speed improved by 75.68%. The proposed method can be applied to SAR target recognition with dual polarization, imbalance samples and low-resolution condition for reference.

[1]  Simon A. Wagner,et al.  SAR ATR by a combination of convolutional neural network and support vector machines , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[3]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[4]  Ding Ju.  Similarity Constrained Deep Belief Networks with Application to SAR Image Target Recognition , 2016 .

[5]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[6]  Odysseas Kechagias-Stamatis,et al.  Fusing Deep Learning and Sparse Coding for SAR ATR , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Henry Leung,et al.  SAR Target Recognition Based on Probabilistic Meta-Learning , 2021, IEEE Geoscience and Remote Sensing Letters.

[8]  Huanxin Zou,et al.  Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation , 2013, IEEE Geoscience and Remote Sensing Letters.

[9]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[10]  Mengdao Xing,et al.  SAR Target Configuration Recognition via Two-Stage Sparse Structure Representation , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Chengliang Zhong,et al.  SAR Target Image Classification Based on Transfer Learning and Model Compression , 2019, IEEE Geoscience and Remote Sensing Letters.

[12]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[13]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[14]  Léon Bottou,et al.  Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.

[15]  Hidetoshi Furukawa,et al.  Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance , 2017, ArXiv.

[16]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[17]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Lei Xue,et al.  RECOGNITION OF SAR TARGET BASED ON MULTILAYER AUTO-ENCODER AND SNN , 2013 .

[19]  Huanxin Zou,et al.  Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[20]  Haipeng Wang,et al.  Target Classification Using the Deep Convolutional Networks for SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jianwei Li,et al.  A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification , 2018, Sensors.

[22]  Zongxu Pan,et al.  Projection Shape Template-Based Ship Target Recognition in TerraSAR-X Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[23]  Jianwei Li,et al.  Ship classification for unbalanced SAR dataset based on convolutional neural network , 2018, Journal of Applied Remote Sensing.

[24]  Wang Heng,et al.  Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition , 2018 .

[25]  Lanqing Huang,et al.  OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[27]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[28]  B. P. Salmon,et al.  Synthetic Aperture Radar Ship Detection Using Haar-Like Features , 2017, IEEE Geoscience and Remote Sensing Letters.