A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection

Dong, Li; Liang, Quanhuan; Liu, Hongqing; Liu, Qinghua; Liu, Haijun; Liao, Guisheng

doi:10.1109/tgrs.2021.3062038

Cited by 55 publications

(34 citation statements)

References 33 publications

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“…To evaluate the performance of our method, we illustrated the capability of the proposed CY-RFB approach on SAR ship images and compared our method with the state-of-the-art methods, including YOLOv1, 22 YOLOv3, 21 CenterNet+, 32 DAPN, 16 Faster-RCNN, 25 ARPN, 27 Quad-FPN, 28 MDDLNet, 29 and YOLOv4. 20 For a fair comparison with our work, these algorithms' parameters are followed by reference papers.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Zhang et al 28 proposed a novel quad feature pyramid network (Quad-FPN) for SAR ship detection, which is an excellent two-stage SAR ship detector. Li et al 29 proposed a novel multidimensional domain deep learning network (MDDLNet) for SAR ship detection, which utilizes complementary features of spatial domain and frequency domain. Although various attempts have been made at ship detection, SAR ship detection remains a challenging problem due to complex background disturbances (such as port facilities, sea clutters, and unstable sea conditions), multiscale ship feature differences, and inconspicuous small ship features.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

Zhang

Yang

et al. 2023

J. Appl. Rem. Sens.

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With the development of synthetic aperture radar (SAR) technology, more SAR datasets with high resolution and large scale have been obtained. Research using SAR images to detect and monitor marine targets has become one of the most important marine applications. In recent years, deep learning has been widely applied to target detection. However, it was difficult to use deep learning to train an SAR ship detection model in complex scenes. To resolve this problem, an SAR ship detection method combining YOLOv4 and the receptive field block (CY-RFB) was proposed in this paper. Extensive experimental results on the SAR-Ship-Dataset and SSDD datasets demonstrated that the proposed method had achieved supreme detection performance compared to the state-of-the-art ship detection methods in complex scenes, whether they were in offshore or inshore scenes of SAR images.

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

Zhang

Yang

et al. 2023

J. Appl. Rem. Sens.

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“…Zhang et al [41] proposed a multitask learning-based object detector (MTL-Det) to distinguish ships in SAR images. Li et al [42] designed a novel multidimensional domain deep learning network and exploited the spatial and frequency-domain complementary features to SAR ship detection. Jiang et al [43] proposed the YOLO-V4-light network using the multi-channel fusion SAR image processing method.…”

Section: Deep Learning-based Horizontal Sar Ship Detection Methodsmentioning

confidence: 99%

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

et al. 2021

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Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.

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“…To address the multiscale ship detection problem, Cui et al [29] proposed a dense attention pyramid network, which combines the salient features with global features to improve detection performance. Besides, Li et al [30] proposed a multidimensional deep learning network considering the features from spatial domain and frequency domain, respectively. Since the setting of anchors in anchor-based detectors directly affects the detection performance, some recent studies introduced anchor-free based detectors.…”

Section: The Problemmentioning

confidence: 99%

Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Bai

Cheng

et al. 2023

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

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Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.

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A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection

Cited by 55 publications

References 33 publications

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

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