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29 December 2008 Multi-temporal MODIS-data-based PSO-FCM clustering applied to wetland extraction in the Sanjiang Plain
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Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72854Z (2008) https://doi.org/10.1117/12.815788
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hanli Liu, Tao Pei, Chenghu Zhou, and A-Xing Zhu "Multi-temporal MODIS-data-based PSO-FCM clustering applied to wetland extraction in the Sanjiang Plain", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72854Z (29 December 2008); https://doi.org/10.1117/12.815788
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