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http://dx.doi.org/10.9766/KIMST.2022.25.1.030

A Dataset of Ground Vehicle Targets from Satellite SAR Images and Its Application to Detection and Instance Segmentation  

Park, Ji-Hoon (Defense AI Technology Center, Agency for Defense Development)
Choi, Yeo-Reum (Defense AI Technology Center, Agency for Defense Development)
Chae, Dae-Young (Defense AI Technology Center, Agency for Defense Development)
Lim, Ho (Defense AI Technology Center, Agency for Defense Development)
Yoo, Ji Hee (Defense AI Technology Center, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.25, no.1, 2022 , pp. 30-44 More about this Journal
Abstract
The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.
Keywords
Synthetic Aperture Radar; Target Detection; Instance Segmentation; Mask R-CNN(), Deep Learning;
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1 Q. Guo, et. al., "Scattering Enhanced Attention Pyramid Network for Aircraft Detection in SAR Images," IEEE Trans. Geosci. Remote Sens., Early Access(DOI: 10.1109/TGRS.2020.3027762), pp. 1-18, 2021.   DOI
2 S. Wei, et. al., "A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation," IEEE Access, Vol. 8, pp. 120234-120254, 2020.   DOI
3 K. He, et. al., "Mask R-CNN," Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961-2969, 2017.
4 Y. Chang, et. al., "Ship Detection Based on YOLOv2 for SAR Imagery," Remote Sens., Vol. 11, No. 786, pp. 1-14, 2017.
5 T. Zhang, et. al., "LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images," Remote Sens., Vol. 12, No. 18, pp. 1-37, 2020.   DOI
6 K. He, et. al., "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 770-778, 2016.
7 K. El-Darymli, et. al., "Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review," IEEE Access, Vol. 4, pp. 6014-6058, 2016.   DOI
8 R. Girshick, et. al., "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580-587, 2014.
9 Y. Zhao, et. al., "Attention Receptive Pyramid Network for Ship Detection in SAR Image," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, Vol. 13, pp. 2738-2756, 2020.   DOI
10 Y. Zhao, et. al., "Pyramid Attention Dilated Network for Aircraft Detection in SAR Images," IEEE Geosci. Remote Sens. Lett., Vol. 18, No. 4, pp. 662-666, 2021.   DOI
11 T. Lin, et. al., "Feature Pyramid Networks for Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 2117-2125, 2017.
12 W. Liu, et. al., "SSD: Single Shot MultiBox Detector," Proceedings of the European Conference on Computer Vision(ECCV), pp. 21-37, 2016.
13 J. Jiao, et. al., "A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection," IEEE Access, Vol. 6, pp. 20881-20892, 2018.   DOI
14 K. El-Darymli, et. al., "Target Detection in Synthetic Aperture Radar Imagery: A State-of-the-Art Survey," J. Appl. Remote Sens., Vol. 7, No. 071598, pp. 1-36, 2013.
15 G. Gao, et. al., "An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images," IEEE Trans. Geosci. Remote Sens., Vol. 47, No. 6, pp. 1685-1697, 2009.   DOI
16 X. Qin, et. al., "A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High-Resolution SAR Images," IEEE Geosci. Remote Sens. Lett., Vol. 10, No. 4, pp. 806-810, 2013.   DOI
17 S. Ren, et. al., "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE PAMI, Vol. 39, No. 6, pp. 1137-1149, 2017.   DOI
18 J. Redmon, et. al., "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 779-788, 2016.
19 C. Chen, et. al., "A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios," IEEE Access, Vol. 7, pp. 104848-104863, 2019.   DOI
20 S. Xian, et. al., "AIR-SARShip-1.0: High-Resolution SAR Ship Detection Dataset," J. Radars, Vol. 8, No. 6, pp. 852-862, 2019.