Analysis on the Azimuth Shift of a Moving Target in Sar Image

Accurately tracking marine traffic considering security and commercial activities is still challenging despite its increasing global importance. Recently, space-borne synthetic aperture radar (SAR) is being considered to accurately monitor maritime traffic, and techniques to detect the position of ships and estimate their velocity have become essential. Here, we investigated the potential for automatic estimation of ship velocity using the azimuth offset between ships and wakes detected using convolutional neural network (CNN) coupled with SAR imagery. We found that azimuth offset is proportional to the Doppler shift effect of the back-scattered signal in SAR, thus, it relates to the radial velocity of a moving target. Consequently, we propose a method whereby a CNN is applied to automatically detect ship wakes from TanDEM-X data. In this method, ship velocity is calculated using the azimuthal distance (i.e., azimuth offset) between the stern of the detected ship and the vertex of the detected V-shape wake-determined as the intersection of two lines obtained through edge filtering and Radon transforms. The location and number of detected ships are then compared with an automatic identification system (AIS), and the calculated velocity of the ship is compared with the velocity obtained via along-track interferometry and AIS. Results show that our method automatically detects ships and wakes with accuracies of 91.0% and 93.2%, respectively, and estimates the velocity of ships with an accuracy of 0.13 m/s. This method is effective when wind velocities are not substantially higher than 5.5 m/s and ship velocities are not extremely low.

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“…According to the equations given in [35] and [36], ship velocity can be calculated using the azimuth offset as follows:…”

Section: B Estimation Of Ship Velocity Using Azimuth Offset From Radon Transformsmentioning

confidence: 99%

Ship Velocity Estimation From Ship Wakes Detected Using Convolutional Neural Networks

Kang

Kim

2019

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

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Specific Windows Search for Multi-Ship and Multi-Scale Wake Detection in SAR Images

Ding¹,

Yang²,

Zhao³

et al. 2021

Remote Sensing

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Traditional ship identification systems have difficulty in identifying illegal or broken ships, but the wakes generated by ships can be used as a major feature for identification. However, multi-ship and multi-scale wake detection is also a big challenge. This paper combines the geometric and pixel characteristics of ships and their wakes in Synthetic Aperture Radar (SAR) images and proposes a method for multi-ship and multi-scale wake detection. This method first detects the highlight pixel area in the image and then generates specific windows around the centroid, thereby detecting wakes of different sizes in different areas. In addition, all wake components can be located completely based on wake clustering, the statistical features of wake axis pixels can be used to determine the visible length of the wake. Test results on the Gaofen-3 SAR image show the special potential of the method for wake detection.

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An Integrated Method for Refocusing of Moving Targets in Spotlight Sar

Papila

Paker

Kartal

2023

A-JESA

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A new target-refocusing technique based-on re-centering phase computation of previously recorded moving target raw data is implemented to the Spotlight SAR data in order to obtain refocused moving targets. The technique is tested on the integrated simulated data; background real spotlight SAR Raw data with the synthetically generated data domes of civilian moving targets. First Polar format Algorithm is applied to detect and estimate the speed of ground-moving targets on the integrated raw data. At the next step, re-organize the integrated raw data by selecting and arranging target focusing center with a new technique based on re-centering phase computation to each moving target speed. At the third step re-organize the raw data by re-centering the phase computation to each moving target location. Finally, Polar Format Algorithm is applied to each reorganized raw data to obtain highly focused moving targets individually

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Analysis on the Azimuth Shift of a Moving Target in Sar Image

Cited by 3 publications

References 31 publications

Ship Velocity Estimation From Ship Wakes Detected Using Convolutional Neural Networks

Ship Velocity Estimation From Ship Wakes Detected Using Convolutional Neural Networks

Specific Windows Search for Multi-Ship and Multi-Scale Wake Detection in SAR Images

An Integrated Method for Refocusing of Moving Targets in Spotlight Sar

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