VideoSAR moving target detection and tracking algorithm based on deep learning

doi:10.6046/zrzyyg.2022126

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Abstract

The video synthetic aperture radar (VideoSAR) technology is widely used in military reconnaissance, geological exploration, and disaster prediction, among other fields. Owing to multiple interference factors in SAR videos, such as speckle noise, specular reflection, and overlay effect, moving targets are easily mixed with background or other targets. Therefore, this study proposed an effective VideoSAR target detection and tracking algorithm. Firstly, several features of VideoSAR were extracted to construct multichannel feature maps. Then, deeper features were extracted using the improved lightweight EfficientDet network, thus improving the accuracy of SAR target detection while considering algorithm efficiency. Finally, the trajectory association strategy based on bounding boxes was employed to associate the same target in VideoSAR. The experimental results show that the method proposed in this study is effective for SAR shadow target detection and tracking.

Keywords VideoSAR feature enhancement target detection deep learning feature pyramid network multi-target tracking

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TP79

Issue Date: 07 July 2023

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	Lei QIU
	Xuezhi ZHANG
	Dawei HAO

Cite this article:

Lei QIU,Xuezhi ZHANG,Dawei HAO. VideoSAR moving target detection and tracking algorithm based on deep learning[J]. Remote Sensing for Natural Resources, 2023, 35(2): 157-166.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022126 OR https://www.gtzyyg.com/EN/Y2023/V35/I2/157

Fig.1 Flowchart of feature analysis

Fig.2 Structure of EfficientNet and the multi-scale feature pyramid

Fig.3 Structure of multi-scale feature fusion

Fig.4 Module of track linking

Fig.5 Structure of Bi-LSTM

Tab.1 Sizes of ships in SAR images

Tab.2 Sizes of shadow targets in Sandia data

Tab.3 Information of the multi-scale feature pyramid on the ship dataset

Tab.4 Information of the multi-scale feature pyramid on the Sandia dataset

Fig.6 The curve of the samples

Fig.7 Comparison results of different filters

Fig.8 Analysis of feature on the Sandia dataset

Tab.5 Comparison of the detection results(%)

Fig.9 Visualization of the ship detection results

Tab.6 Comparison results of shadow targets detection(%)

Fig.10 Visualization of car detection results at two timesteps

Fig.11 A case in which Bi-LSTM finds the missing target

Fig.12 A sample of our track linking results

Tab.7 Results of multi-target tracking(%)

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