Detection of Aircraft Using Sentinel-1 SAR Image Series

Dostovalov, M. Y.; Ermakov, R. V.; Moussiniants, Thomas

doi:10.23919/irs.2018.8448147

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…In the rough detection of SAR aircraft targets, the aim is to detect the real aircraft targets as much as possible. Readers can refer to recent surveys [28], [29], [30] for more details in the SAR aircraft detection. Here, we only review several milestone works.…”

Section: A Sar Aircraft Detection With Deep Learningmentioning

confidence: 99%

“…where According to the shape characteristics of aircraft targets in SAR images, the clustering center is set as 12. After 10 times of clustering analysis, the average clustering results are as follows:(14, 14), (14,21), (20,18), (21,25), (26,21), (29,31), (49, 36), (39, 47), (59, 45), (45, 60), (80, 78), (137, 143). Compared with the hand-designed anchor boxes such as Faster R-CNN [11], anchor boxes obtained by this advanced clustering algorithm can better adapt to the size of the aircraft target.…”

Section: A Detection Networkmentioning

confidence: 99%

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SEFEPNet: Scale Expansion and Feature Enhancement Pyramid Network for SAR Aircraft Detection With Small Sample Dataset

Zhang

Hao

Tian

et al. 2022

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

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Aircraft detection in Synthetic Aperture Radar (SAR) images is still a challenging research task because of the insufficient public data, the difficulty of multi-scale target detection, and the complexity of background interference. In this paper, we construct a public SAR Aircraft Detection Dataset (SADD) with complex background and interference objects to facilitate the research in SAR aircraft detection. Then, we propose the Scale Expansion and Feature Enhancement Pyramid Network (SEFEPNet) as the SADD baseline. It uses a four-scale fusion method to combine the shallow position information with the deep semantic information, effectively adapting to the multiscale target detection in SAR images, significantly improving the detection effect of small targets. The Feature Enhancement Pyramid (FEP) structure is connected behind the backbone network to weaken the background texture and highlight the target to achieve feature enhancement, improving the ability to extract target features in complex backgrounds. Finally, to further improve the detection performance of the small-scale SAR aircraft dataset, we propose a domain adaptive transfer learning method. Experiments on SADD show that this method can significantly improve the recall rate and F1 score. At the same time, we find that the transfer effect of using homologous but different types of targets as the source domain is better than those of heterologous but same types of targets in SAR aircraft detection, which is instructive for future research.

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Section: A Sar Aircraft Detection With Deep Learningmentioning

confidence: 99%