In recent years the artificial neural network has been developed and applied to remotely sensed data classification problem. Most modal of them are error back-propagation(BP)

BP learning algorithm based multi-layer perceptron. Compared to the conventional statistical classifier

BPNN RS image classifier are non-parametric and may have the capacity of more robust proximity especially when distributions are strongly non-Gaussian

but its main shortcoming is its slow training speed

local minimum and even being unable to converge. The Radial Basis Functions Neural Network (RBFNN) modal

integrating the parametric statistic distribution modal and non-parametric single layer perceptron modal

trains faster and more stable than BPNN while keeping the complicated proximity. In this article

the survey and analysis of the RBFNN for the classification of remotely-sensed multi-spectral image is presented

and the RBF RS image classification modal

detailed algorithms and realization procedures is intially raised. The framework which fuses Geo-Knowledge into RBFNN by RBF functions and hierarchical clustering means with optimization evolution theory also are introduced. Finally

the case of practical application of remote sensing land cover classification in Hong Kong region is presented. After the procedure of RBFNN and BPNN approaches are synthetically analyzed

experimental results show that RBFNN approach has more advantages in train time

network structure

knowledge fusion

etc.