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dc.contributor.advisorEl-Sheimy, Naser
dc.contributor.advisorLeung, Henry
dc.contributor.authorMoussa, Adel
dc.date.accessioned2014-01-30T21:37:43Z
dc.date.available2016-02-11T21:13:15Z
dc.date.issued2014-01-30
dc.date.submitted2014en
dc.identifier.citationMoussa, A. (2014). Segmentation and Classification of Multi-Sensor Data Using Artificial Intelligence Techniques (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/27734en_US
dc.identifier.urihttp://hdl.handle.net/11023/1354
dc.description.abstractVast amounts of remotely sensed data from a variety of remote sensors deployed on satellites and other platforms are collected regularly. This includes satellite images, aerial images, and Light Detection And Ranging (LiDAR) data. The continuous advancements of the remote sensing systems offered new enhanced data specifications that need evolving processing techniques to exploit the new abilities, resolutions, and accuracies in highly automatic fashion to overcome the slow costly yet accurate human processing. The main objective of this research is to develop a framework for classification of data acquired from different sensors in object based fashion using a flexible classification engine. A segmentation approach has been proposed where many similarity measures can be used interchangeably or collaboratively. This proposed approach minimizes the needed parameters and avoids the commonly used assumption of the same scale objects. Multiple data layers from different sources and specifications could be employed collaboratively in this approach with the ability of relative weighting of these layers to represent the relevance of each data layer. The performance of this approach has been compared to human segmentation for assessment. The achieved performance shows the significance of the proposed approach. Inspired by the flexibility and expandability of human classification abilities, A flexible classification methodology is proposed that can learn through reference data sets, employ rules in accumulative fashion, tolerate missing features, and extend to include new classes. An adaptive sequential classification approach has been proposed to further improve the classification performance. Several tests of different data sources including satellite images, aerial images, and LiDAR data have been conducted using the proposed classification methodologies. The achieved classification result demonstrates the significance of the proposed segmentation and classification approaches.en_US
dc.language.isoeng
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subjectGeotechnology
dc.subject.classificationSegmentationen_US
dc.subject.classificationClassificationen_US
dc.subject.classificationMulti-sensor dataen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.titleSegmentation and Classification of Multi-Sensor Data Using Artificial Intelligence Techniques
dc.typedoctoral thesis
dc.publisher.facultyGraduate Studies
dc.description.embargoterms2 yearsen_US
dc.publisher.institutionUniversity of Calgaryen
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/27734
thesis.degree.nameDoctor of Philosophy
thesis.degree.namePhD
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
atmire.migration.oldid1911
dc.publisher.placeCalgaryen
ucalgary.item.requestcopytrue


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University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.