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Proceedings Paper

Level set segmentation for greenbelts by integrating wavelet texture and priori color knowledge
Author(s): Tie-jun Yang; Zhi-hui Song; Chuan-xian Jiang; Lin Huang
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Paper Abstract

Segmenting greenbelts quickly and accurately in remote sensing images is an economic and effective method for the statistics of green coverage rate (GCR). Towards the problem of over-reliance on priori knowledge of the traditional level set segmentation model based on max-flow/min-cut Graph Cut principle and weighted Total Variation (GCTV), this paper proposes a level set segmentation method of combining regional texture features and priori knowledge of color and applies it to greenbelt segmentation in urban remote sensing images. For the color of greenbelts is not reliable for segmentation, Gabor wavelet transform is used to extract image texture features. Then we integrate the extracted features into the GCTV model which contains only priori knowledge of color, and use both the prior knowledge and the targets’ texture to constrain the evolving of the level set which can solve the problem of over-reliance on priori knowledge. Meanwhile, the convexity of the corresponding energy functional is ensured by using relaxation and threshold method, and primal-dual algorithm with global relabeling is used to accelerate the evolution of the level set. The experiments show that our method can effectively reduce the dependence on priori knowledge of GCTV, and yields more accurate greenbelt segmentation results.

Paper Details

Date Published: 11 September 2013
PDF: 7 pages
Proc. SPIE 8907, International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications, 89070D (11 September 2013); doi: 10.1117/12.2030725
Show Author Affiliations
Tie-jun Yang, Guilin Univ. of Technology (China)
Zhi-hui Song, Guilin Univ. of Technology (China)
Chuan-xian Jiang, Guilin Univ. of Technology (China)
Lin Huang, Guilin Univ. of Technology (China)


Published in SPIE Proceedings Vol. 8907:
International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications
Haimei Gong; Zelin Shi; Qian Chen; Jin Lu, Editor(s)

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