The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 105–112, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-105-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 105–112, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-105-2020

  21 Aug 2020

21 Aug 2020

OBJECT-BASED IMAGE ANALYSIS OF DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

I. Kotaridis and M. Lazaridou I. Kotaridis and M. Lazaridou
  • Aristotle University of Thessaloniki, Faculty of Engineering, School of Civil Engineering, Lab. of Photogrammetry - Remote Sensing, 54124 Thessaloniki, Greece

Keywords: OBIA, Image segmentation, SVM, Landsat-8, Sentinel-2, QuickBird, Land cover classification

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.