Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 761-764, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-761-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
A FAST SEGMENTATION ALGORITHM FOR C-V MODEL BASED ON EXPONENTIAL IMAGE SEQUENCE GENERATION
J. Hu1,2, L. Lu2, J. Xu1, and J. Zhang2 1College of Geomatics, Shandong University of Science and Technology, Qingdao 266510,China
2Chinese Academy of Surveying & Mapping,Beijing 100830,China
Keywords: SAR, coastline, image sequence generation, exponential C-V model Abstract. For the island coastline segmentation, a fast segmentation algorithm for C-V model method based on exponential image sequence generation is proposed in this paper. The exponential multi-scale C-V model with level set inheritance and boundary inheritance is developed. The main research contributions are as follows: 1) the problems of the "holes" and "gaps" are solved when extraction coastline through the small scale shrinkage, low-pass filtering and area sorting of region. 2) the initial value of SDF (Signal Distance Function) and the level set are given by Otsu segmentation based on the difference of reflection SAR on land and sea, which are finely close to the coastline. 3) the computational complexity of continuous transition are successfully reduced between the different scales by the SDF and of level set inheritance. Experiment results show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which automates the process of coastline segmentation.
Conference paper (PDF, 1730 KB)


Citation: Hu, J., Lu, L., Xu, J., and Zhang, J.: A FAST SEGMENTATION ALGORITHM FOR C-V MODEL BASED ON EXPONENTIAL IMAGE SEQUENCE GENERATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 761-764, https://doi.org/10.5194/isprs-archives-XLII-2-W7-761-2017, 2017.

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