scholarly journals Color Restoration for Full-Waveform Multispectral LiDAR Data

2020 ◽  
Vol 12 (4) ◽  
pp. 593
Author(s):  
Binhui Wang ◽  
Shalei Song ◽  
Wei Gong ◽  
Xiong Cao ◽  
Dong He ◽  
...  
Keyword(s):  
Spatial Information ◽  
Digital Camera ◽  
Color Difference ◽  
Laser Source ◽  
Imaging Data ◽  
Waveform Data ◽  
Multispectral Data ◽  
Full Waveform ◽  
Relative Standard ◽  
Color Restoration

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

Managing the Edentulous Mandible using Recent Technological Developments: A Case Study

2013 ◽  
Vol 2 (2) ◽  
pp. 50-54
Author(s):  
Ashok Sethi ◽  
Thomas Kaus ◽  
Naresh Sharma ◽  
Peter Sochor
Keyword(s):  
Computed Tomography ◽  
Spatial Information ◽  
Imaging Techniques ◽  
Accurate Information ◽  
Imaging Data ◽  
Surgical Guide ◽  
Prosthetic Reconstruction ◽  
Anterior Teeth ◽  
Edentulous Mandible ◽  
Cad Cam

Safe clinical practice in implant dentistry requires an accurate investigation of the availability of bone for implant placement and the avoidance of critical anatomical structures. Modern imaging techniques using computed tomography (CT) and cone beam computed tomography (CBCT) provide the clinician with the required information. The imaging thus obtained provides accurate representation of the height, width and length of the available bone.1 In addition, whenever adequate radiation dose is used, accurate information about the bone density in Hounsfield units can be obtained. Important spatial information regarding the orientation of the ridges and the relationship to the proposed prosthetic reconstruction can be obtained with the aid of radiopaque templates during the acquisition of CT scan data. Modern software also provides the facility to decide interactively upon the positioning of the implants and is able to relate this to a stereolithographic model constructed from the imaging data. A surgical guide for the accurate positioning of the implants can be constructed. The construction of screw retained prostheses is fraught with difficulties regarding the accuracy of the construction. Accurate fit of the prosthesis is difficult to obtain due to the inherent errors in impression taking, component discrepancies, investing and casting inaccuracies.2,3 CAD/CAM technology eliminates the inaccuracies involved with the investing and casting of superstructures. Clinical Case This case describes the management of an 84 year old female patient, who had recently lost her remaining mandibular anterior teeth. This resulted in the patient's inability to wear conventional dentures in the mandible.

scholarly journals REFINED SIMULATION OF SATELLITE LASER ALTIMETER FULL ECHO WAVEFORM

2018 ◽  
Vol XLII-3 ◽  
pp. 1267-1273
Author(s):  
H. Men ◽  
Y. Xing ◽  
G. Li ◽  
X. Gao ◽  
Y. Zhao ◽  
...  
Keyword(s):  
Laser Energy ◽  
Vital Role ◽  
Data Simulation ◽  
Laser Altimeter ◽  
Validation Data ◽  
Simulation Accuracy ◽  
Waveform Data ◽  
Full Waveform ◽  
Ground Target ◽  
Echo Simulation

The return waveform of satellite laser altimeter plays vital role in the satellite parameters designation, data processing and application. In this paper, a method of refined full waveform simulation is proposed based on the reflectivity of the ground target, the true emission waveform and the Laser Profile Array (LPA). The ICESat/GLAS data is used as the validation data. Finally, we evaluated the simulation accuracy with the correlation coefficient. It was found that the accuracy of echo simulation could be significantly improved by considering the reflectivity of the ground target and the emission waveform. However, the laser intensity distribution recorded by the LPA has little effect on the echo simulation accuracy when compared with the distribution of the simulated laser energy. At last, we proposed a refinement idea by analyzing the experimental results, in the hope of providing references for the waveform data simulation and processing of GF-7 satellite in the future.

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

Comparative Experiment of Color Reproduction Methods

2014 ◽  
Vol 1051 ◽  
pp. 967-970
Author(s):  
Qi Jia ◽  
Xu Liang Lv ◽  
Wei Dong Xu ◽  
Jiang Hua Hu ◽  
Xian Hui Rong
Keyword(s):  
Polynomial Regression ◽  
Rapid Development ◽  
Color Space ◽  
Digital Camera ◽  
Color Difference ◽  
High Tech ◽  
Comparative Experiment ◽  
Color Reproduction ◽  
Different Color ◽  
Time Image

Digital camera which has the advantage of real-time image transferring and easily processing is more and more widely used in the packaging and printing industry with the rapid development of high-tech electronics industry. However, the color in digital camera is not accurate which affect the application. To minimize the color difference between the color in the digital camera and the real color, the color reproduction methods is developing. The field comparative experiment is carried out to compare the performance of color reproduction methods, such as polynomial regression algorithm in different color space, and color checker passport. The results show that fourth order polynomial regression color reproduction in XYZ color space has the best performance.

scholarly journals Spectral and spatial information from a novel dual-wavelength full-waveform terrestrial laser scanner for forest ecology

2018 ◽  
Vol 8 (2) ◽  
pp. 20170049 ◽  
Author(s):  
F. Mark Danson ◽  
Fadal Sasse ◽  
Lucy A. Schofield
Keyword(s):  
Field Experiments ◽  
Forest Ecology ◽  
Laser Scanner ◽  
Dual Wavelength ◽  
Leaf Biomass ◽  
Terrestrial Laser Scanner ◽  
Area Index ◽  
Waveform Data ◽  
Full Waveform ◽  
Wide Range

The Salford Advanced Laser Canopy Analyser (SALCA) is an experimental terrestrial laser scanner designed and built specifically to measure the structural and biophysical properties of forest canopies. SALCA is a pulsed dual-wavelength instrument with co-aligned laser beams recording backscattered energy at 1063 and 1545 nm; it records full-waveform data by sampling the backscattered energy at 1 GHz giving a range resolution of 150 mm. The finest angular sampling resolution is 1 mrad and around 9 million waveforms are recorded over a hemisphere above the tripod-mounted scanner in around 110 min. Starting in 2010, data pre-processing and calibration approaches, data analysis and information extraction methods were developed and a wide range of field experiments conducted. The overall objective is to exploit the spatial, spectral and temporal characteristics of the data to produce ecologically useful information on forest and woodland canopies including leaf area index, plant area volume density and leaf biomass, and to explore the potential for tree species identification and classification. This paper outlines the key challenges in instrument development, highlights the potential applications for providing new data for forest ecology, and describes new avenues for exploring information-rich data from the next generation of terrestrial laser scanners instruments like SALCA.

scholarly journals FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3568 ◽  
Author(s):  
Takayuki Shinohara ◽  
Haoyi Xiu ◽  
Masashi Matsuoka
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lidar Data ◽  
Global Features ◽  
Waveform Data ◽  
Full Waveform ◽  
3D Point Clouds ◽  
Waveform Lidar ◽  
Full Waveform Lidar

In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.

scholarly journals Calibration of the Pulse Signal Decay Effect of Full-Waveform Hyperspectral LiDAR

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5263 ◽  
Author(s):  
Zhang ◽  
Gao ◽  
Niu ◽  
Pei ◽  
Bi ◽  
...  
Keyword(s):  
Spatial Information ◽  
Pulse Signal ◽  
Arrival Times ◽  
Range Measurements ◽  
Range Ambiguity ◽  
Signal Decay ◽  
Full Waveform ◽  
Red Edge ◽  
Spectral Channels ◽  
Decay Effect

Full-waveform hyperspectral LiDAR (FWHSL) is able to obtain spectral and spatial information by utilizing a single instrument, and it has become more and more commonly used in vertical distribution studies of structural and biochemical characteristics of vegetation. However, the pulse-echo arrival times of multiple spectral channels of the FWHSL are not consistent and this causes range ambiguity in spectral channels. In this paper, the pulse signal decay effect on range measurements was studied by measuring the varying trends of pulse signal decay between spectral channels with different material properties. The experiments were repeated at different distances. All of the spectral channels were compared for different materials. The results suggest that the channels in the red edge spectral region of vegetation have good stability and accuracy for range measurements of varied distance and materials properties. Finally, based on the geometric invariability in a specific red edge channel, a practical calibration approach for the pulse signal decay effect is also presented. The validation tests showed it could improve the pulse signal decay effect of full-waveform hyperspectral LiDAR.

scholarly journals WATER TURBIDITY ESTIMATION FROM LIDAR BATHYMETRY DATA BY FULL-WAVEFORM ANALYSIS – COMPARISON OF TWO APPROACHES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 681-688
Author(s):  
K. Richter ◽  
D. Mader ◽  
P. Westfeld ◽  
H.-G. Maas
Keyword(s):  
Measurement Data ◽  
Airborne Lidar ◽  
Waveform Analysis ◽  
Estimation Methods ◽  
Data Set ◽  
Water Turbidity ◽  
Waveform Data ◽  
Full Waveform ◽  
Novel Approach ◽  
Airborne Lidar Bathymetry

Abstract. Airborne LiDAR bathymetry is an efficient technique for surveying the bottom of shallow waters. In addition, the measurement data contain valuable information about the local turbidity conditions in the water body. The extraction of this information requires appropriate evaluation methods examining the decay of the recorded waveform signal. Existing approaches are based on several assumptions concerning the influence of the ALB system on the waveform signal, the extraction of the volume backscatter, and the directional independence of turbidity. The paper presents a novel approach that overcomes the existing limitations using two alternative turbidity estimation methods as well as different variants of further processed full-waveform data. For validation purposes, the approach was applied to a data set of a shallow inland water. The results of the quantitative evaluation show, which method and which data basis is best suited for the derivation of area wide water turbidity information.

scholarly journals A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution

2021 ◽  
Author(s):  
Michele Bortolomeazzi ◽  
Lucia Montorsi ◽  
Damjan Temelkovski ◽  
Mohamed Reda Keddar ◽  
Amelia Acha-Sagredo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Performance ◽  
Spatial Information ◽  
Single Cell Analysis ◽  
Biological Tissues ◽  
Imaging Data ◽  
Cell Identification ◽  
Spatially Resolved ◽  
Multiplexed Imaging

ABSTRACTMultiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at: https://github.com/ciccalab/SIMPLI.

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