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Proceedings Paper

Multi-temporal MODIS-data-based PSO-FCM clustering applied to wetland extraction in the Sanjiang Plain
Author(s): Hanli Liu; Tao Pei; Chenghu Zhou; A-Xing Zhu
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Paper Abstract

In order to enhance the spectral characteristics of features for clustering, in the experiment of wetland extraction in Sanjiang Plain, we use a series of approaches in preprocessing of the MODIS remote sensing data by considering eliminating interference caused by other features. First, by analysis of the spectral characteristics of data, we choose a set of multi-temporal and multi-spectral MODIS data in Sanjiang Plain for clustering. By building and applying mask, the water areas and woodland vegetation can be eliminated from the image data. Second, by Enhanced Lee filtering and Minimum Noise Fraction (MNF) transformation, the data can be denoised and the characteristics of wetland can be enhanced obviously. After the preprocessing of data, the fuzzy c-means clustering algorithm optimized by particle swarm algorithm (PSO-FCM) is utilized on the image data for the wetland extraction. The result of experiment shows that the accuracy of wetland extraction by means of PSO-FCM algorithm is reasonable and effective.

Paper Details

Date Published: 29 December 2008
PDF: 11 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72854Z (29 December 2008);
Show Author Affiliations
Hanli Liu, East China Normal Univ. (China)
Institute of Geographic Sciences and Natural Resources Research (China)
Tao Pei, East China Normal Univ. (China)
Institute of Geographic Sciences and Natural Resources Research (China)
Chenghu Zhou, Institute of Geographic Sciences and Natural Resources Research (China)
A-Xing Zhu, Institute of Geographic Sciences and Natural Resources Research (China)
Univ. of Wisconsin, Madison (United States)


Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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