Vessel Detection with SDGSAT-1 Nighttime Light Images
The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most exi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4354 |
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| author | Zheng Zhao Shi Qiu Fu Chen Yuwei Chen Yonggang Qian Haodong Cui Yu Zhang Ehsan Khoramshahi Yuanyuan Qiu |
| author_facet | Zheng Zhao Shi Qiu Fu Chen Yuwei Chen Yonggang Qian Haodong Cui Yu Zhang Ehsan Khoramshahi Yuanyuan Qiu |
| author_sort | Zheng Zhao |
| collection | DOAJ |
| description | The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm. |
| first_indexed | 2024-03-10T23:13:20Z |
| format | Article |
| id | doaj.art-5fef181d1db1476ab9e35b1014b67fe8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:13:20Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5fef181d1db1476ab9e35b1014b67fe82023-11-19T08:48:03ZengMDPI AGRemote Sensing2072-42922023-09-011517435410.3390/rs15174354Vessel Detection with SDGSAT-1 Nighttime Light ImagesZheng Zhao0Shi Qiu1Fu Chen2Yuwei Chen3Yonggang Qian4Haodong Cui5Yu Zhang6Ehsan Khoramshahi7Yuanyuan Qiu8Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAdvanced Laser Technology Laboratory of Anhui Province, Hefei 230601, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaMyyrmäki Campus, School of Smart and Clean Solutions, Metropolia University of Applied Sciences, 01600 Vantaa, FinlandKey Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm.https://www.mdpi.com/2072-4292/15/17/4354SDGSAT-1nighttime light imagelow-light remote sensingvessel detectionrobust PCADBSCAN |
| spellingShingle | Zheng Zhao Shi Qiu Fu Chen Yuwei Chen Yonggang Qian Haodong Cui Yu Zhang Ehsan Khoramshahi Yuanyuan Qiu Vessel Detection with SDGSAT-1 Nighttime Light Images Remote Sensing SDGSAT-1 nighttime light image low-light remote sensing vessel detection robust PCA DBSCAN |
| title | Vessel Detection with SDGSAT-1 Nighttime Light Images |
| title_full | Vessel Detection with SDGSAT-1 Nighttime Light Images |
| title_fullStr | Vessel Detection with SDGSAT-1 Nighttime Light Images |
| title_full_unstemmed | Vessel Detection with SDGSAT-1 Nighttime Light Images |
| title_short | Vessel Detection with SDGSAT-1 Nighttime Light Images |
| title_sort | vessel detection with sdgsat 1 nighttime light images |
| topic | SDGSAT-1 nighttime light image low-light remote sensing vessel detection robust PCA DBSCAN |
| url | https://www.mdpi.com/2072-4292/15/17/4354 |
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