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Proceedings Paper

Research on texture feature of RS image based on cloud model
Author(s): Zuocheng Wang; Lixia Xue
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Paper Abstract

This paper presents a new method applied to texture feature representation in RS image based on cloud model. Aiming at the fuzziness and randomness of RS image, we introduce the cloud theory into RS image processing in a creative way. The digital characteristics of clouds well integrate the fuzziness and randomness of linguistic terms in a unified way and map the quantitative and qualitative concepts. We adopt texture multi-dimensions cloud to accomplish vagueness and randomness handling of texture feature in RS image. The method has two steps: 1) Correlativity analyzing of texture statistical parameters in Grey Level Co-occurrence Matrix (GLCM) and parameters fuzzification. GLCM can be used to representing the texture feature in many aspects perfectly. According to the expressive force of texture statistical parameters and by Correlativity analyzing of texture statistical parameters, we can abstract a few texture statistical parameters that can best represent the texture feature. By the fuzziness algorithm, the texture statistical parameters can be mapped to fuzzy cloud space. 2) Texture multi-dimensions cloud model constructing. Based on the abstracted texture statistical parameters and fuzziness cloud space, texture multi-dimensions cloud model can be constructed in micro-windows of image. According to the membership of texture statistical parameters, we can achieve the samples of cloud-drop. By backward cloud generator, the digital characteristics of texture multi-dimensions cloud model can be achieved and the Mathematical Expected Hyper Surface(MEHS) of multi-dimensions cloud of micro-windows can be constructed. At last, the weighted sum of the 3 digital characteristics of micro-window cloud model was proposed and used in texture representing in RS image. The method we develop is demonstrated by applying it to texture representing in many RS images, various performance studies testify that the method is both efficient and effective. It enriches the cloud theory, and proposes a new idea for image texture representing and analyzing, especially RS image.

Paper Details

Date Published: 7 November 2008
PDF: 11 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470W (7 November 2008); doi: 10.1117/12.813233
Show Author Affiliations
Zuocheng Wang, Chongqing Univ. of Posts and Telecommunications (China)
Peking Univ. (China)
Lixia Xue, Chongqing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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