Dual-Polarized SAR Ship Grained Classification Based on CNN With Hybrid Channel Feature Loss

Zeng, Lijiang; Zhu, Qingtao; Lu, Danwei; Zhang, Tao; Wang, Hongmiao; Yin, Junjun; Yang, Jian

doi:10.1109/lgrs.2021.3067678

Cited by 28 publications

(18 citation statements)

References 23 publications

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“…They found that the N-YOLO model is more competitive than the traditional CNN-based target detection methods. Zeng et al [28] investigated a vessel grain classification for dual-polarization SAR, and their model was able to effectively classify vessels into eight accurate classes, such as cargo, tanker, carrier, container, fishing dredger, tug, passenger, etc., using VV and VH dual-polarization channels to enhance the classification performance on the OpenSARvessel dataset. The application of the dual-polarization idea also provides an important support for later research.…”

Section: Deep-learning-based Vessel Monitoring Methodsmentioning

confidence: 99%

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

Yang

et al. 2023

JMSE

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Vessel monitoring technology involves the application of remote sensing technologies to detect and identify vessels in various environments, which is critical for monitoring vessel traffic, identifying potential threats, and facilitating maritime safety and security to achieve real-time maritime awareness in military and civilian domains. However, most existing vessel monitoring models tend to focus on a single remote sensing information source, leading to limited detection functionality and underutilization of available information. In light of these limitations, this paper proposes a comprehensive ship monitoring system that integrates remote satellite devices and nearshore detection equipment. The system employs ResNet, a deep learning model, along with data augmentation and transfer learning techniques to enable bidirectional detection of satellite cloud images and nearshore outboard profile images, thereby alleviating prevailing issues such as low detection accuracy, homogeneous functionality, and poor image recognition applicability. Empirical findings based on two real-world vessel monitoring datasets demonstrate that the proposed system consistently performs best in both nearshore identification and remote detection. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of different modules and discuss the constraints of current deep learning-based vessel monitoring models.

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Section: Deep-learning-based Vessel Monitoring Methodsmentioning

confidence: 99%

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

Yang

et al. 2023

JMSE

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“…Table 6 lists the results of SBNN with fusion and three other floating-point CNN-based Dual-Polarization SAR ship classification methods in the six-categories task. The counterparts are: VGG with hybrid channel feature loss [15], mini hourglass region extraction and dual-channel efficient fusion network [44] and the squeeze-and-excitation Laplacian pyramid network (SE-LPN-DPFF) [38].…”

Section: Comparison With Cnn-based Sar Ship Classification Methodsmentioning

confidence: 99%

“…A Dual-Polarization SAR classification network based on deep learning and hybrid channel feature loss is proposed in [15]. Features in two different polarized channels are respectively extracted by the same backbone which is also adapted from VGGNet.…”

Section: Introductionmentioning

confidence: 99%

SBNN: A Searched Binary Neural Network for SAR Ship Classification

et al. 2022

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The synthetic aperture radar (SAR) for ocean surveillance missions requires low latency and light weight inference. This paper proposes a novel small-size Searched Binary Network (SBNN), with network architecture search (NAS) for ship classification with SAR. In SBNN, convolution operations are modified by binarization technologies. Both input feature maps and weights are quantized into 1-bit in most of the convolution computation, which significantly decreases the overall computational complexity. In addition, we propose a patch shift processing, which can adjust feature maps with learnable parameters at spatial level. This process enhances the performance by reducing the information irrelevant to the targets. Experimental results on the OpenSARShip dataset show the proposed SBNN outperforms both binary neural networks from computer vision and CNN-based SAR ship classification methods. In particular, SBNN shows a great advantage in computational complexity.

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“…Zhang et al [26] combined HOG features with deep features and produced better recognition results. Zeng et al [27] fused polarization information and deep features to achieve SAR ship recognition. Electromagnetic scattering features, as the intrinsic feature of SAR targets, can better explain and characterize the electromagnetic scattering mechanism of the target by introducing them into deep networks, which has received wide attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition

Zhang,

Feng,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep learning has offered new ideas in SAR ship target recognition. Although many methods improve the recognition performance through the improvement of loss function and migration of deep networks, scattering features as the important intrinsic features of SAR targets, need to be considered in the SAR ship recognition tasks. To introduce the scattering features into the deep network and characterize the features of ship targets more comprehensively, a multi-scale global scattering feature association network (MGSFA-Net) for SAR ship target recognition is proposed in this paper. In the network, the SAR ship target is firstly separated from the background by fine target segmentation. Then, the scattering centers (SCs) of ship targets are extracted and converted to local graph structures based on the k-Nearest Neighbors (KNN) algorithm. The local graph structures are associated by the scattering center feature association (SCFA) module and enhanced by the multi-scale feature enhancement (MSFE) module to produce the multi-scale global scattering features. Moreover, the deep features of the targets are extracted by the multi-kernel deep feature extraction (MKDFE) module to characterize the high-dimensional information. Finally, the scattering features and deep features are fused by weighted integration to enrich the diversity of features. The experimental results on the FUSAR-Ship and OpenSARShip dataset show that, the MGSFA-Net can significantly improve the recognition performance, even on a few-shot condition with the accuracy increasing over 2%-3%. The feature distribution and visualization show the effectiveness of the MGSFA-Net to characterize the multi-scale global scattering association features.

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Dual-Polarized SAR Ship Grained Classification Based on CNN With Hybrid Channel Feature Loss

Cited by 28 publications

References 23 publications

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

SBNN: A Searched Binary Neural Network for SAR Ship Classification

MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition

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