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Grant: Discovery Grants Program - Individual

University of New Brunswick

$54,000.00

Object-based 3D information incorporated classification and change detection

FY 2015-16

Summary
As much as 80 percent of the information used for decision making has some geospatial context. Numerous modern high resolution satellites have been launched since 1999 to improve the capacity of acquiring and updating detailed geospatial information globally. However, due to very limited availability of effective technologies to extract information from the images, many images have become outdated before being effectively used or even being used. Land cover classification and change detection are two most widely used techniques in information extraction from the images. Object-based classification is a newly emerging technology. It has demonstrated tremendous potential, but also encountered many technically challenging problems. Based on the findings and results of my current NSERC projects, this proposed research intends to develop new solutions to mitigate problems existing in the three essential areas of object-based classification and change detection, i.e. object segmentation, object classification, and image registration. New techniques for supervised segmentation, object-based and 3D information incorporated supervised classification, and 3D information incorporated change detection will be developed. 3D information from stereo images will be utilized, as all the modern high resolution satellites and airborne sensors have stereo capacity. Object-based and 3D information incorporated image registration will be developed to overcome existing image registration problems. The successful outcome of this research will contribute to the gap reduction between vastly collected remote sensing images and very limited capacity of information extraction from the images. It will help users to effectively utilize remote sensing images for a broad range of applications, such as environment monitoring, natural resource investigation, planning, and military missions. The education of high quality researchers or engineers in the proposed research area is strategically important for keeping Canada's leading position in geoinformatics.
Program
Discovery Grants Program - Individual
Location
New Brunswick, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
239066-2011
Name nom
Zhang, Yun
Department département
Geodesy and Geomatics Engineering
Organization id
75
Institution établissement
University of New Brunswick
Province en
New Brunswick
Province fr
Nouveau-Brunswick
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2011
Award amount
54000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1509
Committee name en
Civil, Industrial and Systems Engineering
Committee name fr
Génie civil, industriel et des systèmes
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
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
5
Nb partie
5
Application title
Object-based 3D information incorporated classification and change detection
Keyword
NSERC
Application summary
As much as 80 percent of the information used for decision making has some geospatial context. Numerous modern high resolution satellites have been launched since 1999 to improve the capacity of acquiring and updating detailed geospatial information globally. However, due to very limited availability of effective technologies to extract information from the images, many images have become outdated before being effectively used or even being used. Land cover classification and change detection are two most widely used techniques in information extraction from the images. Object-based classification is a newly emerging technology. It has demonstrated tremendous potential, but also encountered many technically challenging problems. Based on the findings and results of my current NSERC projects, this proposed research intends to develop new solutions to mitigate problems existing in the three essential areas of object-based classification and change detection, i.e. object segmentation, object classification, and image registration. New techniques for supervised segmentation, object-based and 3D information incorporated supervised classification, and 3D information incorporated change detection will be developed. 3D information from stereo images will be utilized, as all the modern high resolution satellites and airborne sensors have stereo capacity. Object-based and 3D information incorporated image registration will be developed to overcome existing image registration problems. The successful outcome of this research will contribute to the gap reduction between vastly collected remote sensing images and very limited capacity of information extraction from the images. It will help users to effectively utilize remote sensing images for a broad range of applications, such as environment monitoring, natural resource investigation, planning, and military missions. The education of high quality researchers or engineers in the proposed research area is strategically important for keeping Canada's leading position in geoinformatics.