Ship target tracking based on a low-resolution optical satellite in geostationary orbit

Liu, Yong; Yao, Libo; Xiong, Wei; Jing, Tian; Zhang, Zhimin

doi:10.1080/01431161.2018.1437296

Cited by 15 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, this paper focuses on two main techniques: one is multitarget motion shadow rejection for multitarget sports video, and the other is multitarget motion precision tracking for multitarget sports video. e detection analysis of fixed camera includes the fast acquisition of data in sports video, collection of event information of target groups in sports video, sports video event refinement, and extraction of those targets that are more interested in tracking target features and are in sports video multitarget motion state; then according to the actual situation and needs, the relevant targets are systematically divided and each target to be tracked is targeted tracking [19,20].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

View full text Add to dashboard Cite

The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Reviewing the literature of authors who operated on higher resolution images, very few of them utilize CFAR techniques. However, it appears to be the dominant method for optical images which exceed a resolution of 40m GSD, with other papers including those by Liu et al [8,9] abilities when operating on high resolution imagery, but go on to state that they may not be feasible for lower resolutions due to a lack of available features [8]. The use of deep learning techniques for low resolution vessel detection was not investigated in detail in their study however, which invites an investigation of their claims in this paper.…”

Section: A Vessel Detection Methods Based On Low Resolution Satellite Imagerymentioning

confidence: 95%

“…Despite this result, it is difficult to compare the performance of their algorithm with others, given the differences in test methodology and lack of test metrics provided. They test their algorithm on simulated vessel images when quoting the algorithms performance [10], unlike the others, who use real captured images of vessels at sea [4,9]. Ghosh et al [4] provide more information when evaluating their performance, although the number of test images used is not mentioned.…”

Section: Comparable Performancesmentioning

confidence: 99%

Self-Supervised Vessel Detection from Low Resolution Satellite Imagery

Farr

Petrunin

Kakareko

et al. 2022

AIAA SCITECH 2022 Forum

View full text Add to dashboard Cite

Maritime surveillance is a growing field of application for optical imaging satellites. In this paper, we aim to increase the practical effectiveness of vessel detection algorithms by developing a computationally inexpensive method that can discern vessels in low resolution optical satellite imagery. This creates an opportunity to host the algorithm on-board small, lowcost nanosatellites, generating feasibility for large surveillance constellations. The presented algorithm, which is based upon a deep convolutional autoencoder utilized as an anomaly detector, was found to achieve a detection precision of 98%, and a recall of 79%. Data used to test and train the algorithm was produced by augmenting ESA Sentinel-2 imagery, reducing the resolution to mimic that of a nanosatellite's. The results presented here are compared with comparable papers in the field, where it is demonstrated that the proposed algorithm is capable of outperforming classical techniques in terms of detection precision. For an equivalent resolution, the proposed algorithm provides a 34% increase in precision over the Constant False Alarm Rate (CFAR) technique.

show abstract

“…An individual GF-4 scene can cover an area of 400 km × 400 km, with a spatial resolution of about 50 m and 400 m for the PMS and IRS, respectively [2]. GF-4 was designed to operate in gazing image mode, and thus the revisit period can reach 20 s [3]. GF-4 can obtain a series of time-continuous images over the same area, which provides an ideal observation approach for the detection of changing and moving targets.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cloud Detection Method for GF-4 Imagery

Lü

Zhan

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

show abstract

Ship target tracking based on a low-resolution optical satellite in geostationary orbit

Cited by 15 publications

References 20 publications

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Self-Supervised Vessel Detection from Low Resolution Satellite Imagery

An Improved Cloud Detection Method for GF-4 Imagery

Contact Info

Product

Resources

About