Remote sensing ocean data analyses using fuzzy C-Means clustering

Suqin Xu; Jie Chen

doi:10.1117/12.833199

30 October 2009 Remote sensing ocean data analyses using fuzzy C-Means clustering

Suqin Xu, Jie Chen

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74980S (2009) https://doi.org/10.1117/12.833199
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

With the deep understanding and exploitation of the wide Ocean, There are more and more fine instrument installed or loaded on measuring ships or other marines. The high costs and complexity of corrosion place ever-increasing demands on the analyses of surrounding ocean environment. In this paper, the fuzzy C-Means clustering is used to analyze the surrounding ocean environment with remote sensing data. The studied ocean area is considered as a two dimensional gird or an image, and the fuzzy C-Means clustering technique is used to reveal the underlying relationship of the elements and segment the interrelated ocean in regions with similar spectral properties in the influence of instrument corrosion. The influence of the environment elements in instrument corrosion is studied and a priori spatial information is added to improving the segmentation result. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization and the segmentation could be accomplished. The calculation results show that the segmentation is accurate and reasonable. This ocean environment analysis fruit has used in real application and has proved to be valuable in ship instrument corrosion monitoring and the guide of other ocean activity.

Citation Download Citation

Suqin Xu and Jie Chen "Remote sensing ocean data analyses using fuzzy C-Means clustering", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74980S (30 October 2009); https://doi.org/10.1117/12.833199

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