Exploring the atmosphere with lidars

Veerabuthiran, S.

doi:10.1007/bf02867129

Cited by 2 publications

(4 citation statements)

References 1 publication

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Section: Types Of Lidarmentioning

confidence: 99%

“…Based on the nature of the scattered light, many types of LiDAR, including Rayleigh, Mie, Raman and resonance fluorescence, have been described (Veerabuthiran, 2003; Zhang et al ., 2011). Both Rayleigh and Mie scattering are elastic, meaning both incidence and scattered light have the same wavelength, and no energy is lost as the incidence light impinges on the particles (Veerabuthiran, 2003). For Rayleigh, the incident light has a much longer wavelength than the size of the impinged particle, while Mie scattering occurs when the particle has much larger size than the wavelength of the impinging light (Veerabuthiran, 2003).…”

Section: Types Of Lidarmentioning

confidence: 99%

“…Both Rayleigh and Mie scattering are elastic, meaning both incidence and scattered light have the same wavelength, and no energy is lost as the incidence light impinges on the particles (Veerabuthiran, 2003). For Rayleigh, the incident light has a much longer wavelength than the size of the impinged particle, while Mie scattering occurs when the particle has much larger size than the wavelength of the impinging light (Veerabuthiran, 2003). Unlike Rayleigh and Mie scattering, Raman scattering is inelastic and involves energy (or wavelength) shift, based on the molecular nature of the particles impinged by the light; the Raman energy shift is unique to different molecules and could be used to characterize different particles (Turner et al ., 2016).…”

Section: Types Of Lidarmentioning

confidence: 99%

“…Based on the nature of the scattered light, four basic types of LiDAR, including Rayleigh, Mie, Raman and fluorescence have been described (Veerabuthiran, 2003; Zhang et al ., 2011). The laser‐induced optical behaviours have been exploited for the detection and characterization of biogenic materials both in laboratory and field conditions using LiDAR platforms.…”

Section: Introductionmentioning

confidence: 99%

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Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance

Owoicho

Olwal

Quaye

2020

Microbial Biotechnology

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Viruses remain a significant public health concern worldwide. Recently, humanity has faced deadly viral infections, including Zika, Ebola and the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The threat is associated with the ability of the viruses to mutate frequently and adapt to different hosts. Thus, there is the need for robust detection and classification of emerging virus strains to ensure that humanity is prepared in terms of vaccine and drug developments. A point or stand-off biosensor that can detect and classify viruses from indoor and outdoor environments would be suited for viral surveillance. Light detection and ranging (LiDAR) is a facile and versatile tool that has been explored for stand-off detection in different environments including atmospheric, oceans and forest sensing. Notably, laser-induced fluorescence-light detection and ranging (LIF-LiDAR) has been used to identify MS2 bacteriophage on artificially contaminated surgical equipment or released amidst other primary biological aerosol particles in laboratory-like close chamber. It has also been shown to distinguish between different picornaviruses. Currently, the potentials of the LIF-LiDAR technology for real-time stand-off surveillance of pathogenic viruses in indoor and outdoor environments have not been assessed. Considering the increasing applications of LIF-LiDAR for potential microbial pathogens detection and classification, and the need for more robust tools for viral surveillance at safe distance, we critically evaluate the prospects and challenges of LIF-LiDAR technology for real-time stand-off detection and classification of potentially pathogenic viruses in various environments.

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Section: Types Of Lidarmentioning

confidence: 99%

Section: Types Of Lidarmentioning

confidence: 99%

Section: Types Of Lidarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance

Owoicho

Olwal

Quaye

2020

Microbial Biotechnology

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Lidar detection of underwater objects using a neuro-SVM-based architecture

Mitra

Wang

Banerjee

2006

IEEE Trans. Neural Netw.

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This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

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Exploring the atmosphere with lidars

Cited by 2 publications

References 1 publication

Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance

Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance

Lidar detection of underwater objects using a neuro-SVM-based architecture

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