Analytical Formula to Investigate the Modulation of Sloped Targets Using LiDAR Waveform

Hu, Yihua; Hou, Ahui; Ma, Qiong; Zhao, Nanxiang; Xu, Shilong; Fang, Jiajie

doi:10.1109/tgrs.2021.3099478

Cited by 9 publications

(6 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…휔 0 = 2 * 휆/(푝푖 * 휑), 휑 is the beam divergence angle. Therefore, for a single target surface element when the laser interacts with the target, the modulation formula for the echo waveform is [12] :…”

Section: 1multispectral Waveform Modelingmentioning

confidence: 99%

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

Dong,

Xu,

Xia

2023

AOPC 2023: Laser Technology and Applications; And Optoelectronic Devices and Integration

View full text Add to dashboard Cite

Section: 1multispectral Waveform Modelingmentioning

confidence: 99%

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

Dong,

Xu,

Xia

2023

AOPC 2023: Laser Technology and Applications; And Optoelectronic Devices and Integration

View full text Add to dashboard Cite

“…With the emergence and development of lidar, tomography algorithm is introduced, which improves the accuracy and efficiency of laser imaging technology. At present, the commonly used classical algorithms include iRadon transform [3], FBP [10][11][12][13], and ART [14]. However, the iRadon transform algorithm can only restore the image roughly, which is far from the ideal imaging result.…”

Section:  mentioning

confidence: 99%

“…Therefore, in the case of the complete angle, the proposed algorithm greatly improves the resolution of the reconstructed image and reduces the image artifacts. For the projection data in the near-field experiment, the proposed TV sparse imaging reconstruction method in Section 3.2 is performed compared with FBP [10][11][12][13], ART [14], and sparse ART with a complete view of projections and uniformly sampled projections in 5 • , 10 • , and 20 • viewing intervals. The results are shown in Figure 7.…”

Section: Near-field Experimentsmentioning

confidence: 99%

“…Radon-Fourier [3,9] transform is the basic tomographic algorithm, but its accuracy is low, and the reconstructed image has obvious artifacts. Therefore, the FBP algorithm [10][11][12][13] has become the most commonly used algorithm for laser reflection tomography. However, in the space environment, almost all space targets are non-cooperative targets, which significantly affects the result of the FBP algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

et al. 2022

Self Cite

View full text Add to dashboard Cite

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.

show abstract

“…Analytic estimates of architecture performance can be derived from simplifications of the lidar equation, assuming mean surface reflectance and atmospheric transmittance [10,13]. For simple reflectance geometries, explicit formulas for the change in waveform are tractable [17]. When applied to many microfacets, the aggregation of differential changes in the transmitted waveform results in realistic return waveforms [17,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network

Sipps,

Magruder

2023

Remote Sensing

View full text Add to dashboard Cite

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at an arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at the global scale due to computational expense. Instead of predicting the arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty, specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network Gaussian mixture model, also known as a mixture density network (MDN). Training data were created from differencing geocorrected Global Ecosystem Dynamics Investigation (GEDI) L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on the regional distribution predictive capability. On average, the relative error of the equivalent standard deviation of the predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlates to a qualitative understanding of prediction accuracy and instrument performance.

show abstract

Analytical Formula to Investigate the Modulation of Sloped Targets Using LiDAR Waveform

Cited by 9 publications

References 53 publications

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

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

Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network

Contact Info

Product

Resources

About