Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 757-761, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-757-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 757-761, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-757-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2016

21 Jun 2016

LAND COVER MAPPING USING SENTINEL-1 SAR DATA

S. Abdikan1, F. B. Sanli2, M. Ustuner2, and F. Calò3 S. Abdikan et al.
  • 1Department of Geomatics Engineering, Bulent Ecevit University, 67100 Zonguldak, Turkey
  • 2Department of Geomatics Engineering, Yildiz Technical University, 34220 Esenler-Istanbul, Turkey
  • 3National Research Council (CNR) of Italy – Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), Diocleziano 328, 80124 Napoli, Italy

Keywords: Sentinel-1A, Synthetic Aperture Radar, Land cover classification, Support Vector Machines, Istanbul, Turkey

Abstract. In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.