Grant: Collaborative Research and Development Grants
Ryerson University
$40,000.00
Development of classification tools for multi-spectral LiDAR data
FY 2017-18
Summary
Multi-Spectral Light Detection and Ranging (LiDAR) sensors are a new and emerging technology serving several civil, industrial and military applications. With the launch of the new Teledyne Optech sensor ""Titan"", applications using LiDAR technology entered a new era. The Titan sensor is a laser scanner sensor which operates in three different wavelengths simultaneously (1550, 1064, and 532 nm) and allows recording of object properties from the reflectance of the laser energy of objects. The use of the back scatter energy of the LiDAR sensor pulses (intensity data) has been extensively explored over the last few years. Recent research showed the potential use of the intensity data in land cover classification. However, extensive processing is required to radiometrically correct the intensity data and there is a need for special classification algorithms to maximize the benefit from the use of the LiDAR intensity data. The proposed project will contribute to the Titan technology by further developing and enhancing the radiometric correction models of the LiDAR intensity data. The research team will collaborate with the industrial partner to embed its developments of the radiometric correction process within the process of data collection and delivery. The proposed project is also aiming to develop new image and point classification algorithms that use both the range and intensity data collected by the new sensor for land cover classification. Special focus is given to develop algorithms that work efficiently for land/water delineation automatically from the LiDAR multispectral data. The project will have a direct impact on the LiDAR industry, taking the lead on facilitating the use of multi-spectral LiDAR data. It will also support Canadian leadership in the laser industry.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
488260-2015
Name nom
AbdElrahman, AhmedShaker
Department département
Civil Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2015
Award amount
40000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1004
Research subject group en
Civil engineering
Research subject group fr
Génie civil
Research subject en
Survey engineering and remote sensing
Research subject fr
Génie de l'arpentage et télédétection
Partie
3
Nb partie
3
Application title
Development of classification tools for multi-spectral LiDAR data
Keyword
NSERC
Application summary
Multi-Spectral Light Detection and Ranging (LiDAR) sensors are a new and emerging technology serving several civil, industrial and military applications. With the launch of the new Teledyne Optech sensor ""Titan"", applications using LiDAR technology entered a new era. The Titan sensor is a laser scanner sensor which operates in three different wavelengths simultaneously (1550, 1064, and 532 nm) and allows recording of object properties from the reflectance of the laser energy of objects. The use of the back scatter energy of the LiDAR sensor pulses (intensity data) has been extensively explored over the last few years. Recent research showed the potential use of the intensity data in land cover classification. However, extensive processing is required to radiometrically correct the intensity data and there is a need for special classification algorithms to maximize the benefit from the use of the LiDAR intensity data. The proposed project will contribute to the Titan technology by further developing and enhancing the radiometric correction models of the LiDAR intensity data. The research team will collaborate with the industrial partner to embed its developments of the radiometric correction process within the process of data collection and delivery. The proposed project is also aiming to develop new image and point classification algorithms that use both the range and intensity data collected by the new sensor for land cover classification. Special focus is given to develop algorithms that work efficiently for land/water delineation automatically from the LiDAR multispectral data. The project will have a direct impact on the LiDAR industry, taking the lead on facilitating the use of multi-spectral LiDAR data. It will also support Canadian leadership in the laser industry.