Moving objects detection on satellite video

Yuan-bo, 于渊博 Yu; Tao, 张涛 Zhang; Li-hong, 郭立红 Guo; Xiao-jun, 贺小军 He

doi:10.3788/yjyxs20173202.0138

Cited by 3 publications

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“…In order to make the experiment more complete, we selected four consecutive five frames in the area for comparison. The comparison results are shown in table 2: It can be seen from Table 2 that compared with the algorithm in [16], the comprehensive precision rate has increased from 45% to 95%, and the comprehensive index has increased from 75% to 94%. Compared with the algorithm in [18], the comprehensive precision rate has increased from about 53% to about 95%, the recall rate is increased from about 75% to about 93%, and the comprehensive index has increased from 63% to about 94%.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

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A moving vehicles extraction method in Satellite Videos

Dong

Yang

2020

MATEC Web Conf.

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With the continuous innovation of optical remote sensing technology and the increasing demand for spatial information, satellite videos, which can provide higher spatial and temporal resolution, have been paid a lot of attention. And moving vehicles extraction in satellite videos is one of the most important tasks. By analyzing the shortcomings of current satellite video moving vehicles extraction algorithms, and combining with the characteristics of satellite videos and moving vehicles, this paper proposes an algorithm to extract moving vehicles in satellite videos, that some vehicles are firstly separated from the background by using image extreme points and mean differences, and then the moving vehicles are extracted by joint detection of inter-frame vehicles motion. At the same time, based on the extracted moving vehicles, we also propose a method that can extract road masks by using only three frames. Finally, we use Jilin-1 satellite video data to prove the proposed methods in the experiment. And also this paper has compared the propose methods with another two algorithms, where the results show that the proposed methods greatly improve the accuracy and quality of moving vehicles detection in satellite videos.

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Section: Comparison Of Resultsmentioning

confidence: 99%

“…The traditional moving vehicles detection algorithms mainly include inter-frame difference method [14], background difference method [15] and optical flow method [16]. Many researchers have tried to improve these algorithms and apply them to extract vehicles in remote sensing videos.…”

Section: Introductionmentioning

confidence: 99%

A moving vehicles extraction method in Satellite Videos

Dong

Yang

2020

MATEC Web Conf.

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Research on Monocular Vision Positioning Technology Used in Water Meter Verification

Gao¹,

Sun²,

Lü³

2018

2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

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A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images

Yao

Liu

2018

Sensors

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The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.

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Moving objects detection on satellite video

Cited by 3 publications

References 0 publications

A moving vehicles extraction method in Satellite Videos

A moving vehicles extraction method in Satellite Videos

Research on Monocular Vision Positioning Technology Used in Water Meter Verification

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images

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