Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

DEMİR N., BAYRAM B., Şeker D. Z., Oy S., Ince A., Bozkurt S.

MARINE GEODESY, cilt.42, sa.2, ss.166-185, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 2
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/01490419.2019.1581861
  • Dergi Adı: MARINE GEODESY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.166-185
  • Anahtar Kelimeler: Data fusion, remote sensing, SAR, shoreline detection, MEAN SHIFT, CLASSIFICATION, COASTLINE
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study's authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10 m) by evaluation with the used reference vector data.