Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion DOI Creative Commons
Rong Tian, Yuhang Wang,

Qiguang Zhu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 295 - 295

Published: Jan. 15, 2025

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar technology is being carried out. This paper reports sequential multimodal adaptive algorithm based spatiotemporal sequence fusion, which has strong information extraction noise filtering ability can reconstruct depth reflective intensity from complex echo photon time counts spatial pixel relationships. The method consists three steps: data preprocessing, sequence-optimized extreme value inference filtering, collaborative variation strategy image optimization to achieve high-quality in environments. Simulation test results show that outperforms current imaging algorithms, built system achieves lateral distance resolution 5 mm 2.5cm@6AL, respectively. indicates a great advantage sparse counting possesses capability under background light noise. It also provides good solution targets with long-distance high-resolution.

Language: Английский

Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion DOI Creative Commons
Rong Tian, Yuhang Wang,

Qiguang Zhu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 295 - 295

Published: Jan. 15, 2025

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar technology is being carried out. This paper reports sequential multimodal adaptive algorithm based spatiotemporal sequence fusion, which has strong information extraction noise filtering ability can reconstruct depth reflective intensity from complex echo photon time counts spatial pixel relationships. The method consists three steps: data preprocessing, sequence-optimized extreme value inference filtering, collaborative variation strategy image optimization to achieve high-quality in environments. Simulation test results show that outperforms current imaging algorithms, built system achieves lateral distance resolution 5 mm 2.5cm@6AL, respectively. indicates a great advantage sparse counting possesses capability under background light noise. It also provides good solution targets with long-distance high-resolution.

Language: Английский

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