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Description / Abstract
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science
The Fire Monitoring and Assessment Platform (FireMAP) uses post-fire, aerial imagery to determine fire severity. Traditionally, post fire analysis is done by on-site wildland firefighters, satellites, or manned aircraft. Because traditional post-fire image acquisition is often dangerous for firefighters and too expensive and low resolution from satellites and aircraft, FireMAP plans to use drones for safer and higher resolution post-fire image acquisition. The purpose of this section of the FireMAP project is to transform classified imagery into a form more usable to end users. By the time the aerial imagery has reached this section of the project, each pixel from a post-fire image has been placed into a raster and classified as white ash, black ash, dirt, surface vegetation or canopy vegetation. Within the classified image, the burn area boundaries contain sub-object in size spatiospectral clusters resulting in unclear and incorrectly classified pixels leaving a salt-and-peppered effect across the image. The open source program OpenCV’s open and close morphological functions fix these problems by smoothing burn area boundaries making them clearly defined. Because high severity burns leave areas of white ash smaller than the burnt vegetation, the same morphological functions dilate the high severity burn areas and delete misclassified burn areas.Download this Resource
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Copyright NotesNonexclusive right to publish on file at NNU Library. Available on request.
Keywordsfire monitoring and assessment platform (FireMAP), computer science, image processing
Degree AwardedBachelor of Science
Awarding InstitutionNorthwest Nazarene University
Committee MembersDale Hamilton, Brice Allen
Supervisor or ChairBarry Myers
Peer Reviewed?No
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NNU Computer Science Theses
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