The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 293–300, 2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 293–300, 2022
30 May 2022
30 May 2022


I. Yalcin1,2, G. Karakas1,3, S. Kocaman3,4, S. Saunier5, and C. Albinet6 I. Yalcin et al.
  • 1Hacettepe University, Graduate School of Science and Engineering, Beytepe, Ankara, Turkey
  • 2Hacettepe University, Baskent OSB Technical Sciences Vocational School, 06909 Sincan Ankara, Turkey
  • 3Hacettepe University, Department of Geomatics Engineering, 06800 Beytepe Ankara, Turkey
  • 4ETH Zurich, Institute of Geodesy and Photogrammetry, 8093 Zurich, Switzerland
  • 5Telespazio France, 26 Avenue Jean François Champollion, 31100 Toulouse, France
  • 6European Space Agency, ESRIN, Via Galileo Galilei, 1, 00044 Frascati RM, Italy

Keywords: Image Classification, MAXAR HD, Land Use/Land Cover, Random Forest, Support Vector Machine

Abstract. The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.