Clustering Analysis on Disease Severity of Wheat Stripe Rust Based on SOM Neural Network

摘要：

A SOM (Self-organizing Feature Maps) model was introduced to cluster and analysis on the disease severity of wheat stripe rust based on PHI (Pushbroom hyperspectral imager) data. By means of acquiring the spectral index data (SID) and spectral angle data (SAD) of the samples, combining with the samples spectral average reflectance data (ARD), three twodimensional data matrixes were obtained as the inputs of SOM model. After iterative learning and selforganized clustering, the models outputs farthest approached to the reality in 3-dimensional severity space of wheat stripe rust Then, with the nettrained, all data of the trial plot were simulated. The simulating results demonstrate that the division of wheat stripe rust severity is obviously. The whole trial spot was derived into four grades and the results are satisfactory.

关键词： SOM neural network Clustering analysis PHI image Wheat stripe rust Disease severity

作者: Yang Ke-ming Xue Zhao-hui Li Hong-wei Cui Li Ran Ying-ying Zhang Yong-jie

作者单位: Dept. of Remote sensing and GIS, China University of Mining & Technology (Beijing) Bejing 100083, China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 432-436

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）