In order to break through the limitations of imaging devices and to resolve the problems of Super-Resolution Reconstruction (SRR) of a satellite image

an image reconstruction based on the Radial Basis Function Neural Network (RBFNN) is proposed. First

learning sample images are acquired according to a satellite image observation model and the vector mapping is established to speed up the convergence of RBFNN. Then

the nearest neighbor clustering algorithm is used to dynamically establish the centers and widths of RBF

and decide adaptively the number of hidden layers and connection weights of a net

which are very important parameters for RBFNN. The method can improve the performance of SRR of satellite image and speed up the convergence of RBFNN to 221 s. Experimental results of simulation and generalization indicate that the well-trained RBFNN can realize the SRR of satellite images in higher spatial resolutions

higher efficiencies and lower errors.