Toronto Metropolitan University
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Automated extraction of buildings from IKONOS imagery by integrating spectral and spatial information

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posted on 2021-06-08, 09:55 authored by Xin Min Wang
Very High Resolution (VHR) imagery is becoming indispensable in urban mapping and land management. But owing to its characteristics, traditional spectral-based building extraction methods are not appropriate. This thesis presents an automated spectral-spatial integration extraction approach to extract two-dimensional roof outlines of urban buildings from IKONOS imagery. It substantially improves accuracy, compared with the well-known Extraction and Classification for Homogenous Objects (ECHO) and Iterative Self-Organizing Data Analysis Technique (ISODATA). With spectral-based Multi-peak Supervised Segmentation (MSS), the approach generates first stage classification. In the second stage, the original colour image and the first stage result are fed into a spatial structure classifier. With analysis of overlapped neighborhood's degree of membership to classes, homogeneity and cross-neighborhood structure, the first stage classification is significantly improved. Finally, buildings are extracted with post-processing. Compared with ISODATA, Signature Editor and ECHO, unique contributions are three new algorithms: MSS, Overlapped Neighborhood Analysis, and Cross-neighborhood Structure Detection.

History

Language

eng

Degree

  • Master of Applied Science

Program

  • Civil Engineering

Granting Institution

Ryerson University

LAC Thesis Type

  • Thesis

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    Civil Engineering (Theses)

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