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dc.contributor.advisorChen, ZhiQiang
dc.contributor.authorAryal, Sameer
dc.date.issued2020
dc.date.submitted2020 Spring
dc.descriptionTitle from PDF of title page viewed May 29, 2020
dc.descriptionThesis advisor: ZhiQiang Chen
dc.descriptionVita
dc.descriptionIncludes bibliographical references (pages 60-72)
dc.descriptionThesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020
dc.description.abstractNumerous optical-imaging and machine-vision based inspection methods are found that aim to replace visual and human-based inspection with an automated or a highly efficient procedure. However, these machine-vision systems have not been entirely endorsed by civil engineers towards deploying these techniques in practice, partially due to their poor performance in object detection when structural cracks coexist with other complex scenes. A mobile hyperspectral imaging system is developed in this work, which captures hundreds of spectral reflectance values at a pixel in the visible and near-infrared (VNIR) portion of the electromagnetic spectrum bands. To prove its potential in discriminating complex objects, a machine learning methodology is developed with classification models that are characterized by four different feature extraction processes. Experimental validation with quantitative measures proves that hyperspectral pixels, when used conjunctly with dimensionality reduction, possess outstanding potential in recognizing eight different structural surface objects including cracks for concrete and asphalt surfaces, and outperform the gray-values that characterize the texture/shape of the objects. The authors envision the advent of computational hyperspectral imaging for automating structural damage inspection, especially when dealing with complex structural scenes in practice.
dc.description.tableofcontentsIntroduction -- Hyperspectral Image -- Preprocessing -- Methodology -- Machine Learning Approach -- Discussion -- Appendix 1-2
dc.format.extentx, 109 pages
dc.identifier.urihttps://hdl.handle.net/10355/73784
dc.subject.lcshHyperspectral imaging
dc.subject.lcshMachine learning
dc.subject.lcshStructural analysis (Engineering)
dc.subject.otherThesis -- University of Missouri--Kansas City -- Engineering
dc.titleMobile hyperspectral imaging for structural damage detection
thesis.degree.disciplineCivil Engineering (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelMasters
thesis.degree.nameM.S. (Master of Science)


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