Research on Dynamic K-means Clustering Algorithm in Cyanobacteria Blooms Detection

摘要：

Cyanobacteria blooms are constantly observed in the coastal waters and pose an enormous threat to public health, economy and ecological environment. The characteristics of blue algal bloom images and feature extraction procedures are analyzed in this paper. The pixel value of Cyanobacteria blooms color images has a significant difference from normal coastal waters images, particularly those of Hue and Saturation. A new method is proposed for Cyanobacteria blooms detection using dynamic K-means algorithm. Experimental results demonstrate the excellent practicability of the proposed detection method. Based on the pixel statistics, it can achieve a highly successful probability of detecting bloom images. Therefore, the proposed detection method can be expected to classify and detect Cyanobacteria blooms in monitoring and forecasting systems.

关键词： Cyanobacteria blooms pixel value K-means algorithm image detection

作者: Xu Yang Xiaorong Chen Yiting Li

作者单位: University of Shanghai for Science and Technology, Shanghai 200093 China Infinite Shanghai Communication Terminals Ltd., Shanghai, China

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Applied Mechanics(2011年机电一体化与应用力学国际会议 ICMAM 2011)

会议地点: 香港

会议语种:英文

页码: 428-432

在线出版日期: 2011-12-27（万方平台首次上网日期，不代表论文的发表时间）