Ranging accuracy improvement of time-correlated signal-photon counting lidar

2017 ◽  
Author(s):  
Zijing Zhang ◽  
Yuan Zhao ◽  
Jiandong Zhang ◽  
longzhu Cen ◽  
Shuo Li ◽  
...  
Keyword(s):  
Photon Counting ◽  
Accuracy Improvement ◽  
Signal Photon

scholarly journals Efficient and Noise Robust Photon-Counting Imaging with First Signal Photon Unit Method

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 229
Author(s):  
Kangjian Hua ◽  
Bo Liu ◽  
Zhen Chen ◽  
Liang Fang ◽  
Huachuang Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Likelihood Estimation ◽  
Depth Image ◽  
Noise Robustness ◽  
Least Mean Square ◽  
Tv Regularization ◽  
Imaging Application ◽  
Signal Photon ◽  
Noise Robust

Efficient photon-counting imaging in low signal photon level is challenging, especially when noise is intensive. In this paper, we report a first signal photon unit (FSPU) method to rapidly reconstruct depth image from sparse signal photon counts with strong noise robustness. The method consists of acquisition strategy and reconstruction strategy. Different statistic properties of signal and noise are exploited to quickly distinguish signal unit during acquisition. Three steps, including maximum likelihood estimation (MLE), anomaly censorship and total variation (TV) regularization, are implemented to recover high quality images. Simulations demonstrate that the method performs much better than traditional photon-counting methods such as peak and cross-correlation methods, and it also has better performance than the state-of-the-art unmixing method. In addition, it could reconstruct much clearer images than the first photon imaging (FPI) method when noise is severe. An experiment with our photon-counting LIDAR system was conducted, which indicates that our method has advantages in sparse photon-counting imaging application, especially when signal to noise ratio (SNR) is low. Without the knowledge of noise distribution, our method reconstructed the clearest depth image which has the least mean square error (MSE) as 0.011, even when SNR is as low as −10.85 dB.

scholarly journals Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas

2019 ◽  
Vol 11 (4) ◽  
pp. 471 ◽  
Author(s):  
Yue Ma ◽  
Wenhao Zhang ◽  
Jinyan Sun ◽  
Guoyuan Li ◽  
Xiao Wang ◽  
...  
Keyword(s):  
Land Cover ◽  
Detection Method ◽  
Photon Counting ◽  
Coastal Areas ◽  
Photon Detection ◽  
Land Cover Types ◽  
Pamlico Sound ◽  
Signal Photon ◽  
Adaptive Signal

Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future.

Accuracy improvement of imaging lidar based on time-correlated single-photon counting using three laser beams

2018 ◽  
Vol 429 ◽  
pp. 175-179 ◽  
Author(s):  
Zhaodong Chen ◽  
Rongwei Fan ◽  
Xudong Li ◽  
Zhiwei Dong ◽  
Zhigang Zhou ◽  
...  
Keyword(s):  
Single Photon ◽  
Photon Counting ◽  
Laser Beams ◽  
Accuracy Improvement ◽  
Single Photon Counting

Tantalum superconducting tunnel junctions for photon counting detectors

2002 ◽  
Vol 12 (3) ◽  
pp. 145-148
Author(s):  
C. Jorel ◽  
P. Feautrier ◽  
J.-C. Villégier ◽  
A. Benoit
Keyword(s):  
Tunnel Junctions ◽  
Photon Counting ◽  
Superconducting Tunnel Junctions ◽  
Photon Counting Detectors

Spektrale Photon-Counting Computertomografie für Mehrphasen-Leberdiagnostik mit zwei Kontrastmitteln

2017 ◽  
Vol 189 (S 01) ◽  
pp. S1-S124
Author(s):  
D Münzel ◽  
H Daerr ◽  
R Proksa ◽  
A Fingerle ◽  
P Douek ◽  
...  
Keyword(s):  
Photon Counting

Spektrale Photon-Counting Computertomografie: Anwendung für die virtuelle Koloskopie ohne Abführen

2017 ◽  
Vol 189 (S 01) ◽  
pp. S1-S124
Author(s):  
D Münzel ◽  
D Bar-Ness ◽  
E Roessl ◽  
A Fingerle ◽  
H Daerr ◽  
...  
Keyword(s):  
Photon Counting ◽  
Virtuelle Koloskopie
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