Mixed Image Cell Decomposition Based on Radial-basis Function Neural Networks
基于径向基函数神经网络的混合像元分解

Remote sensing images contain a lot of mixed image cells, and it is difficult to classify these cells. Mixed image cells decomposition algorithm based on principle component analysis is a widely used algorithm, but the large computation amount and less flexibility are its main drawbacks. By researching the curve fitting (approximation)theory of the radial basis function neural networks, and the principles of the mixed image cells decomposition algorithm based on principle component analysis, the paper proposes a new decomposition algorithm, which uses the radial basis function neural networks to fit (approximate) the hyperplane of the decomposition results of the principle component analysis algorithm. Experimental results prove that the results of the new algorithm are almost the same with the results of the principle component analysis algorithm (correlation coefficients are above 0.99). However, the new algorithm has much less computation complexity and more flexibility than the principle component analysis algorithm.