A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Zhang, Gang; Li, Zhi; Li, Xuewei; Chao, Yin; Shi, Zengkai

doi:10.1109/access.2020.3041372

Cited by 19 publications

(11 citation statements)

References 49 publications

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“…For more detailed explanation of overlapping calculation, refer to Dasari and Gorthi (2020). Previous research suggests that Re rate and Pr rate are two popular yet efficient metrics to measure object detection performance such as ship detection (Zhang et al., 2020). Re demonstrates correct positive ship detection ratio of the ship detection model, while Pr shows the ship detection accuracy.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

et al. 2021

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Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

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Section: Experiments and Resultsmentioning

confidence: 99%

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

et al. 2021

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“…The SVM and RF methods based on statistical characteristics show good performance with the fewest ships missed, especially the small ships in VV polarization; however, some false alarms cannot be avoided. The PFN module and feature fusion strategy are often used to improve the detection accuracy and reduce the false alarms of the small target [26,42,52]. Furthermore, those modules always integrate into the VGG16 and ResNet-50 networks [42,53]; the CNN models are complex and have many parameters to train.…”

Section: Discussionmentioning

confidence: 99%

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Geng

Shi

et al. 2021

Remote Sensing

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Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.

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“…Although the obtained physical features and geometric structures using the polarization scattering characteristics methods are richer than those obtained using statistical methods, it is difficult to obtain and process polarization information. The ship target detection method based on deep learning has high accuracy and strong applicability, but it has high requirements regarding the sample set and can only be trained on a labeled dataset [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for SAR Ship Detection Based on Eigensubspace Projection

Shu

Chang

et al. 2022

Remote Sensing

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Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness.

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A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Cited by 19 publications

References 49 publications

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

A Novel Method for SAR Ship Detection Based on Eigensubspace Projection

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