Abstract
Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.
Highlights
It is widely accepted that valid geospatial information are in high demand and need to be updated regularly
Object-Based Image Analysis (OBIA) has been developed since the establishment of very high spatial resolution imagery, where the pixel size is notably smaller than the object concerned
Fewer and fewer Remote Sensing scientists support that further improvements in satellite sensors' spatial resolution will provide more accurate results
Summary
It is widely accepted that valid geospatial information are in high demand and need to be updated regularly In this context, various satellite imagery manipulation approaches have been deployed including Object-Based Image Analysis (OBIA). Various satellite imagery manipulation approaches have been deployed including Object-Based Image Analysis (OBIA) It has the main goal of providing sufficient and automated procedures in order to analyze the imagery by using spectral, textural, spatial and topological characteristics (Blaschke et al, 2008). The preliminary stage of the procedure, the level of unnecessary detail decreases, the image complexity is reduced and the image content becomes more understandable The products of this procedure are the image objects which are the main methodological component of object-based image analysis and represent a real-world object, whilst a pixel cannot (Blaschke et al, 2008). One of the main goals of merging pixels into objects is to get rid of the “salt and pepper” noise (Blaschke, 2010)
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