Dr. Yu-hao Xia Profile

Dr. Yu-hao Xia

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Proceedings Article | 13 December 2024 Paper

Laser reflection tomography algorithm based on photon detection characterization echo

Jin Wang, Yihua Hu, Yuhao Xia, Zhijun Chen, Fei Han

Proceedings Volume 13501, 135010H (2024) https://doi.org/10.1117/12.3048146

KEYWORDS: Ultrasound reflection tomography, Photodetectors, Single photon detectors, Time correlated photon counting, Target detection, Optical simulations, Pulsed laser operation

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Traditional optical imaging systems are limited by diffraction and pixel resolution, while computational imaging techniques have overcome these constraints, emerging as an effective means for super-resolution imaging. Laser Reflection Tomography (LRT), as an active detection method, utilizes laser pulses to illuminate targets from multiple angles and reconstructs images based on the Fourier Slice Theorem. The imaging resolution of LRT is determined solely by signal-to-noise ratio, laser pulse width, and detector bandwidth, and is not affected by detection distance or optical aperture. However, when detecting distant non-cooperative targets, the echo signal energy of LRT decays rapidly with increasing distance, significantly reducing the signal-to-noise ratio and sharply degrading image quality, making it difficult to meet the demands of super-resolution imaging. Leveraging the immense potential of single-photon detection for long-range detection and complex scene perception, this paper proposes a long-range LRT imaging algorithm based on single-photon detection echoes, which includes: (1) The photon detection probability waveform is obtained by calculating the number of photons in each time grid at each detection angle.(2) The waveform data under a series of detection angles are converted into reconstructed targets using algorithms such as filtered back projection.(3) Compare the quality of reconstructed images under different target scales and photon numbers, and analyze the factors affecting the reconstruction quality. Simulation experiments have demonstrated that the algorithm achieves image reconstruction based on photon detection echo characterization for the target model, effectively addressing the issue of image quality degradation due to weak echo energy, and holds significant research value and application potential in long-range detection imaging and space exploration.

Proceedings Article | 18 December 2023 Paper

Analysis and exploration of spatial and spectral information with small-footprint hyperspectral lidar returns

Jia-jie Dong, Shi-long Xu, Yu-hao Xia

Proceedings Volume 12959, 129590D (2023) https://doi.org/10.1117/12.3003887

KEYWORDS: LIDAR, Target detection, Pulse signals, Evolutionary algorithms, Detection and tracking algorithms, Target recognition, Optical simulations, Hyperspectral target detection, Emission wavelengths, Reflectivity

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Full-waveform hyperspectral light detection and ranging (FWHSL) is vital in retrieving spatial and spectral information during laser scanning. Four main influence factors, the distance between two neighbor targets, the coverage ratio, the incident angle, and the target's reflectance, determine the information of the FWHSL returns. Previous studies mainly focus on the influence of the neighbor distance, incident angle, and reflectance, while we focus on the coverage ratio of the targets in a laser footprint. We propose a novel multispectral waveform decomposition method, including the Trust-Region algorithm for single wavelength waveform decomposition, 3σ rule for screening decomposition results and correction between multispectral waveform decomposition, to obtain the accurate spatial and spectral information from the multispectral returns, which realizes the decomposition error less than 0.3cm when the neighbor distance is 40cm, for a 4ns pulse width LiDAR signal. We find the intensity and overlapped ratio of the returns are strongly related to the coverage ratio, which may accelerate the progress in point cloud information extraction and target recognition.

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