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http://dx.doi.org/10.7780/kjrs.2016.32.5.6

Water body extraction using block-based image partitioning and extension of water body boundaries  

Ye, Chul-Soo (Department of Aviation and IT Convergence, Far East University)
Publication Information
Korean Journal of Remote Sensing / v.32, no.5, 2016 , pp. 471-482 More about this Journal
Abstract
This paper presents an extraction method for water body which uses block-based image partitioning and extension of water body boundaries to improve the performance of supervised classification for water body extraction. The Mahalanobis distance image is created by computing the spectral information of Normalized Difference Water Index (NDWI) and Near Infrared (NIR) band images over a training site within the water body in order to extract an initial water body area. To reduce the effect of noise contained in the Mahalanobis distance image, we apply mean curvature diffusion to the image, which controls diffusion coefficients based on connectivity strength between adjacent pixels and then extract the initial water body area. After partitioning the extracted water body image into the non-overlapping blocks of same size, we update the water body area using the information of water body belonging to water body boundaries. The update is performed repeatedly under the condition that the statistical distance between water body area belonging to water body boundaries and the training site is not greater than a threshold value. The accuracy assessment of the proposed algorithm was tested using KOMPSAT-2 images for the various block sizes between $11{\times}11$ and $19{\times}19$. The overall accuracy and Kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.
Keywords
Water Body Extraction; Mahalanobis Distance; Supervised Classification; Mean Curvature Diffusion;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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