中文摘要：

从径向基函数神经网络的理论出发,针对高光谱数据的特点,设计了有效的特征提取模型,再与径向基函数神经网络的输入层连接,建立了一个新的径向基函数神经网络的高光谱遥感影像分类模型,并用国产OMISII传感器获得的64波段数据进行试验。首先进行了最小噪声分离变换,提取了1～20个分量的数据,使用提取后的数据（20维）、提取后数据的纹理变换（20维）和主成分分析的前（20维）,组成了60维向量数据进行分类处理,这种分类器结构简单、容易训练、收敛速度快,其分类精度达到69.27%,高于BP神经网络分类算法（51.20%）以及常用的最小距离分类（MDC）算法（40.88%）。通过对结果和过程进行分析,实验证明径向基函数神经网络在高光谱遥感分类中具有较好的适用性。

英文摘要：

Based on the radial basis function neural network （RBFNN） theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS Ⅱ made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction （MNF） was conducted, and the former 20 components were extracted for further processing. The original data （20 dimension） of extraction by MNF, the texture transformation data （20 dimension） extracted from the former 20 components after MNF, and the principal component analysis data （20 dimension） of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network （BPNN） and 40.88% of traditional minimum distance classification （MDC）, so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.