The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 25–31, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 25–31, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016

  22 Jun 2016

22 Jun 2016

LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME

W. Cao1, X. H. Tong1, S. C. Liu1, and D. Wang2 W. Cao et al.
  • 1Tongji University, College of Survey and Geo-informatic, 200092, Shanghai, P.R. China
  • 2Zhejiang Design Institute of Water Conservancy and Hydroelectric Power, 310002, Hangzhou, P.R. China

Keywords: Landslides Extraction, Semantic Reasoning, High Resolution Imagery, First Order Logic, Disaster Management, Prover9

Abstract. Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should consider primarily the semantic aspects of the data. In this paper, we propose a scheme for recognizing landslides by using an object-oriented analysis technique and a semantic reasoning model on high spatial resolution optical imagery. Three case regions with different data sources are presented to evaluate its practicality. The procedure is designed as follows: first, the Gray Level Co-occurrence Matrix (GLCM) is used to extract texture features after the image explanation. Spectral features, shape features and thematic features are derived for semiautomatic landslide recognition. A semantic reasoning model is used afterwards to refine the classification results, by representing expert knowledge as first-order logic (FOL) rules. The experimental results are essentially consistent with the experts’ field interpretation, which demonstrate the feasibility and accuracy of the proposed approach. The results also show that the scheme has a good generality on diverse data sources.