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Abstract
The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. The tools embedded in eCognition enabled to perform image segmentation and objects classification processes in a semi-automated regime, which is useful for the city planning, spatial analysis and urban growth analysis. The combination of the OBIA method together with technical tools of the eCognition demonstrated applicability of this method for urban mapping in densely populated areas, e.g. in megapolis and capital cities. The methodology included multiresolution segmentation and classification of the created objects.
DOI
https://doi.org/10.31223/osf.io/ge347
Subjects
Computer Sciences, Earth Sciences, Education, Educational Methods, Engineering, Environmental Education, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geographic Information Sciences, Geography, Graphics and Human Computer Interfaces, International and Area Studies, Life Sciences, Natural Resources Management and Policy, Other Computer Sciences, Other Earth Sciences, Other Environmental Sciences, Other Physical Sciences and Mathematics, Physical and Environmental Geography, Physical Sciences and Mathematics, Remote Sensing, Science and Mathematics Education, Social and Behavioral Sciences, Spatial Science, Sustainability
Keywords
remote sensing, GIS, OBIA, image classification, object based image analysis, geoinformatics, Image Analysis, Satellite Image, eCognition, Geoinformation, Image Segmentation
Dates
Published: 2019-01-25 01:28
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