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《Science of Surveying and Mapping》 2012-06
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Image classification of remote sensing based on BP neural networks

LU Liu-ye,ZHANG Qing-feng,LI Guang-lu(College of Resources and Environment,Northwest A&F University,Shaanxi Yangling 712100,China)  
Remote sensing image classification technology is an important segment of image analysis and interpretation.B PANN has the characteristic of faster convergence speed and stronger capabilities of self-study and self-adaptation,it's able to utilize the priori knowledge of data sets furthest to automatically determine reasonable models,and model prediction results can be realistic to reflect the real surface features.This paper adopted Landsat TM images for data sources,Dingxiang country of Shanxi province was taken as the research area,principal component analysis was used to compress the data,a comprehensive model of B PANN integrated NDVI with texture features was adopted to classify regional land use information,finally,the result was compared with the classification based on the method of spectrum cell information and Texture Features-based B PANN separately.The result showed that the accuracy of this method is 80.50%,which increases by18.89% compared with spectrum cell information based ANN classification,and 6.23% compared with texture features based ANN classification separately.It could effectively solve the problems of spectral confusion and low classification accuracy.
【Fund】: 中央高校基本科研业务费(QN2009040);; 陕西省自然科学基金项目(2011JM5007)
【CateGory Index】: TP751
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