Object-oriented coastline classification and extraction from remote sensing imagery

Xizhi Ge; Xiliang Sun; Zhaoqin Liu

doi:10.1117/12.2063845

14 May 2014 Object-oriented coastline classification and extraction from remote sensing imagery

Xizhi Ge, Xiliang Sun, Zhaoqin Liu

Proceedings Volume 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China; 91580M (2014) https://doi.org/10.1117/12.2063845
Event: Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 2012, Wuhan, China

Abstract

Fast and accurate extraction of coastline is of great significance to the management of sea area. And object-oriented multi-scale segmentation method is used for automated extraction and classification coastlines from remote sensing imagery. Classification and extraction rule sets on coastal zone and coastline are set up according to their interpretation signs. Instantaneous waterline is extracted according to extraction rule sets; and a buffer zone to the inner land around this waterline is generated on the basis of extraction result; then coastal zone types are determined through classification. Artificial shoreline and bedrock shoreline are extracted firstly by their characteristics and the coastal zone classification results. Then coastal zone is re-segmented with artificial shoreline and bedrock coastline used as intervention mask, based on which sandy shoreline and developed mucky shoreline can be extracted. Tasseled cap transformation is applied to enhance the extraction result of vegetation on the non-developed muddy coastal beach, which can then be used to extract the non-developed muddy shoreline by rules sets. The experimental results show that the object-oriented classification and extraction method is effective for extraction of artificial shoreline, bedrock shoreline, sandy shoreline and muddy shoreline.

Citation Download Citation

Xizhi Ge, Xiliang Sun, and Zhaoqin Liu "Object-oriented coastline classification and extraction from remote sensing imagery", Proc. SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 91580M (14 May 2014); https://doi.org/10.1117/12.2063845

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