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Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR.

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Title: Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR.
Authors: Yang, Liu1,2,3 (AUTHOR), Zhu, Shouzheng1,2 (AUTHOR), Wang, Ceyuan1,2,3 (AUTHOR), Zhang, Yangyang1,2,4 (AUTHOR), Yang, Wenhang1,2 (AUTHOR), Liu, Xu1,2 (AUTHOR), Hu, Chenhui1,2 (AUTHOR) huchenhui@ucas.ac.cn, He, Xin1,2 (AUTHOR), Wang, Senyuan1,2 (AUTHOR), Li, Siliang1,2 (AUTHOR), Cui, Zhao1,2 (AUTHOR), Li, Chunlai1,2,3 (AUTHOR), Wang, Jianyu1,2,3 (AUTHOR), Chen, Yuwei1,4 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1772. 32p.
Subjects: Spectral sensitivity, Spectral reflectance, Turbidity, Automatic target recognition, LIDAR, Photon counting, Remote sensing
Abstract: Highlights: What are the main findings? Wavelength-dependent ranging bias in turbid water originates from forward-scattering-induced centroid shifts, rather than true spatial displacements. Target discrimination capability is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than by absolute photon counts. What are the implications of the main findings? Multidimensional spectral feature spaces enable underwater material classification that is robust to turbidity-induced signal variations, providing a theoretical basis for turbidity-robust target recognition. The design principle for underwater spectral LiDAR should shift from merely maximizing signal strength to optimizing spectral matching, thereby guiding adaptive wavelength selection in next-generation systems. The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m−1 and 2.0 m−1), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Liu%22">Yang, Liu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Shouzheng%22">Zhu, Shouzheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Ceyuan%22">Wang, Ceyuan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yangyang%22">Zhang, Yangyang</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Wenhang%22">Yang, Wenhang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xu%22">Liu, Xu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Chenhui%22">Hu, Chenhui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> huchenhui@ucas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Xin%22">He, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Senyuan%22">Wang, Senyuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Siliang%22">Li, Siliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Zhao%22">Cui, Zhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chunlai%22">Li, Chunlai</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jianyu%22">Wang, Jianyu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuwei%22">Chen, Yuwei</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1772. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Spectral+sensitivity%22">Spectral sensitivity</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+reflectance%22">Spectral reflectance</searchLink><br /><searchLink fieldCode="DE" term="%22Turbidity%22">Turbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+target+recognition%22">Automatic target recognition</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Photon+counting%22">Photon counting</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Wavelength-dependent ranging bias in turbid water originates from forward-scattering-induced centroid shifts, rather than true spatial displacements. Target discrimination capability is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than by absolute photon counts. What are the implications of the main findings? Multidimensional spectral feature spaces enable underwater material classification that is robust to turbidity-induced signal variations, providing a theoretical basis for turbidity-robust target recognition. The design principle for underwater spectral LiDAR should shift from merely maximizing signal strength to optimizing spectral matching, thereby guiding adaptive wavelength selection in next-generation systems. The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m−1 and 2.0 m−1), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18111772
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      – Code: eng
        Text: English
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        PageCount: 32
        StartPage: 1772
    Subjects:
      – SubjectFull: Spectral sensitivity
        Type: general
      – SubjectFull: Spectral reflectance
        Type: general
      – SubjectFull: Turbidity
        Type: general
      – SubjectFull: Automatic target recognition
        Type: general
      – SubjectFull: LIDAR
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      – SubjectFull: Photon counting
        Type: general
      – SubjectFull: Remote sensing
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      – TitleFull: Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR.
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              M: 06
              Text: Jun2026
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