Moving Ship Detection Method Based on Multi-scale Dual-neighborhood Saliency for GF-4 Satellite Remote Sensing Images

YU Wei; YOU Hongjian; HU Yuxin; LIU Rui

doi:10.11999/JEIT211107

Volume 45 Issue 1

Jan. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(1): 282-290

YU Wei, YOU Hongjian, HU Yuxin, LIU Rui. Moving Ship Detection Method Based on Multi-scale Dual-neighborhood Saliency for GF-4 Satellite Remote Sensing Images[J]. Journal of Electronics & Information Technology, 2023, 45(1): 282-290. doi: 10.11999/JEIT211107

Citation:

YU Wei, YOU Hongjian, HU Yuxin, LIU Rui. Moving Ship Detection Method Based on Multi-scale Dual-neighborhood Saliency for GF-4 Satellite Remote Sensing Images[J]. Journal of Electronics & Information Technology, 2023, 45(1): 282-290. doi: 10.11999/JEIT211107

Citation:

PDF( 4576 KB)

Moving Ship Detection Method Based on Multi-scale Dual-neighborhood Saliency for GF-4 Satellite Remote Sensing Images

doi: 10.11999/JEIT211107

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3.
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China

Received Date: 2021-10-11
Accepted Date: 2022-03-01
Rev Recd Date: 2022-02-27

Available Online: 2022-03-14

Publish Date: 2023-01-17

Abstract

Abstract

The GEostationary Orbit(GEO) GF-4 satellite has the ability to observe continuously moving ships at sea. Ship targets are often weak in the optical remote sensing images of GF-4 satellite, making it difficult to detect directly. By analyzing the wake characteristics of moving ships, a moving ship detection method based on Multi-scale Dual-neighborhood Saliency Model (MDSM) is proposed. First, the saliency of the image is calculated based on MDSM. Then, the position of the moving ship is extracted by adaptive segmentation threshold. Finally, the shape of the candidate target is verified to remove further the false target. Experimental results and analysis show that the proposed method can effectively detect multiple moving targets in GF-4 satellite images, and has better detection performance compared with the current mainstream visual saliency algorithms.
- GF-4 satellite,
- Remote sensing image,
- Ship detection,
- Multi-scale Dual-neighborhood Saliency Model(MDSM)

FullText(HTML)

References(13)

References

[1]	LI Bo, XIE Xiaoyang, WEI Xingxing, et al. Ship detection and classification from optical remote sensing images: A survey[J]. Chinese Journal of Aeronautics, 2021, 34(3): 145–163. doi: 10.1016/j.cja.2020.09.022
[2]	孟令杰, 郭丁, 唐梦辉, 等. 地球静止轨道高分辨率成像卫星的发展现状与展望[J]. 航天返回与遥感, 2016, 37(4): 1–6. doi: 10.3969/j.issn.1009-8518.2016.04.001 MENG Lingjie, GUO Ding, TANG Menghui, et al. Development status and prospect of high resolution imaging satellite in geostationary orbit[J]. Spacecraft Recovery &Remote Sensing, 2016, 37(4): 1–6. doi: 10.3969/j.issn.1009-8518.2016.04.001
[3]	WANG Mi, CHENG Yufeng, CHANG Xueli, et al. On-orbit geometric calibration and geometric quality assessment for the high-resolution geostationary optical satellite GaoFen4[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125: 63–77. doi: 10.1016/j.isprsjprs.2017.01.004
[4]	ZHANG Zhixin, SHAO Yun, TIAN Wei, et al. Application potential of GF-4 images for dynamic ship monitoring[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 911–915. doi: 10.1109/LGRS.2017.2687700
[5]	王晓辉, 胡玉新, 吕鹏. 基于显著图融合的高分四号光学遥感图像多运动舰船检测方法[J]. 中国科学院大学学报, 2021, 38(5): 649–659. doi: 10.7523/j.issn.2095-6134.2021.05.009 WANG Xiaohui, HU Yuxin, and LÜ Peng. Multiple moving ships detection method based on saliency map fusion for GF-4 satellite remote sensing image[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 649–659. doi: 10.7523/j.issn.2095-6134.2021.05.009
[6]	LIU Yong, YAO Libo, XIONG Wei, et al. GF-4 satellite and automatic identification system data fusion for ship tracking[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(2): 281–285. doi: 10.1109/LGRS.2018.2869561
[7]	YAO Libao, LIU Yong, and HE You. A novel ship-tracking method for GF-4 satellite sequential images[J]. Sensors, 2018, 18(7): 2007. doi: 10.3390/s18072007
[8]	XIAO Fengqi, YUAN Fei, and CHENG En. Detection and tracking method of maritime moving targets based on geosynchronous orbit satellite optical images[J]. Electronics, 2020, 9(7): 1092. doi: 10.3390/electronics9071092
[9]	DIAO Wenhui, SUN Xian, ZHENG Xinwei, et al. Efficient saliency - based object detection in remote sensing images using deep belief networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 137–141. doi: 10.1109/LGRS.2015.2498644
[10]	ZHU Hu, NI Haopeng, LIU Shiming, et al. TNLRS: Target-aware non-local low-rank modeling with saliency filtering regularization for infrared small target detection[J]. IEEE Transactions on Image Processing, 2020, 29: 9546–9558. doi: 10.1109/TIP.2020.3028457
[11]	CHEN C L P, LI Hong, WEI Yantao, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574–581. doi: 10.1109/TGRS.2013.2242477
[12]	WEI Yantao, YOU Xinge, and LI Hong. Multiscale patch-based contrast measure for small infrared target detection[J]. Pattern Recognition, 2016, 58: 216–226. doi: 10.1016/j.patcog.2016.04.002
[13]	HOU Xiaodi and ZHANG Liqing. Saliency detection: A spectral residual approach[C]. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007: 1–8.