Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night

XUE Chengcheng; GAO Caixia; HU Jian; QIU Shi; WANG Qi

doi:10.11999/JEIT220369

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1747-1757

XUE Chengcheng, GAO Caixia, HU Jian, QIU Shi, WANG Qi. Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369

Citation:

XUE Chengcheng, GAO Caixia, HU Jian, QIU Shi, WANG Qi. Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1747-1757. doi: 10.11999/JEIT220369

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Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night

doi: 10.11999/JEIT220369

XUE Chengcheng^{1, 2},
GAO Caixia¹,
HU Jian^{1
,
,},
QIU Shi¹,
WANG Qi¹

1.
Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Key Research and Development Program of China (2018YFB0504600), The Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDB-SSW-JSC051)

Received Date: 2022-03-31
Rev Recd Date: 2022-06-20

Available Online: 2022-06-30

Publish Date: 2023-05-10

Abstract

Abstract

To address the problem of insufficient target feature mining in nighttime marine boat detection based on low light remote sensing images, a new sparsity index is designed to minimize the misclassification of boat lights samples and background noises samples, and a detection algorithm for boat lights based on sparse coding and dictionary learning is proposed in this paper. The proposed algorithm is applied to the northern sea area of the Gulf of Mexico, the sea area south of Tianjin Port, and the sea area east of Shanghai Port, and the detection precision is 96.36%, 95.12%, 86.26%, recall rate is 96.36%, 92.86%, 94.19%, and the harmonic mean is 96.36%, 93.98%, 90.05% respectively. Furthermore, the proposed algorithm is compared with three typical marine boat lights detection method for low light remote sensing images during night, demonstrating that the proposed algorithm has a superior performance and provides a new idea for marine boat detection during night.
- Low light remote sensing,
- Target detection,
- Boat detection,
- Sparse representation

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References(40)

References

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