ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 53–58, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-53-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 53–58, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-53-2020

  03 Aug 2020

03 Aug 2020

LAND COVER EXTRACTION OF COASTAL AREA FROM GF-1 WFV IMAGERY USING ONTOLOGICAL METHOD

H. Luo1,2, B. He1,2, X. Kuai1,2, Y. Li1,2, and R. Z. Guo1,2 H. Luo et al.
  • 1Institution of Smart Cities, Shenzhen University, 518060, Shenzhen, China
  • 2Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, 518060, Shenzhen University, Shenzhen, China

Keywords: ontology, land cover, GF-1, Wide Field of View (WFV) image, information extraction

Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.