The application of hyperspectral imagery (HSI) is quite limited to its low spatial resolution. A new method for super-resolution mapping of HSI is proposed using support vector machine(SVM) and wavelet transform. Firstly, spectral unmixing is processed for HSI and the fraction images are obtained. Then the wavelet decomposition is processed on these fraction images. In the local window, the relation between the three high-frequency coefficients of the center pixel and low-frequency coefficients of neighbour pixels are described by training samples, which are used for the learning process of SVM. The trained SVM models are utilized to predict the super-resolution mapping results of the coarse resolution images, i.e., the fraction images. Experiment results show that using wavelet transform can eliminate the dependence on prior information, and compared with the learning method based on BP neural network, SVM can produce higher accuracy super-resolution mapping results.