Uncertainty classification method of remote sensing image based on high-dimensional cloud model and RBF neural network
Youchuan Wan · Cehui kexue · 2012
Cloud model is an uncertainty conversion model between qualitative concept described by natural language and its quantitative expression.The RBF neural network has been applied widely to remote sensing image classification.Considering the traditional RBF neural network classification technique couldn't effectively express uncertainty existing in image classification,and couldn't determine adaptively hidden layer neurons,this paper proposed an uncertainty classification technique based on high-dimension cloud model and improved RBF neural network.Firstly,by using high-dimensional normal cloud models to construct hidden layer neurons,RBF neural network could fully express the uncertainty existing in image classification.Then,by using peak-based cloud transform and high-dimensional cloud algorithm,the optimal neurons of hidden layer were adaptively determined.Finally,by using probability-based weight determination and frequency threshold adjustment,the RBF neural network was further optimized.The experiments showed that the proposed method had higher classification accuracy and could produce good classification results which were consistent with visual interpretation of the human eye.