Classification of Hyperspectral Images Using Wavelet Networks

In our previous works, the wavelet-based feature extraction algorithms have been developed to explore the useful information for the hyperspectral image classification. On the other hand, the idea of using artificial neural network (ANNs) has also proved useful for hyperspectral image classification. To combine the advantages of ANNs with wavelet-based feature extraction methods, the wavelet network (WN) has been proposed for data identification and classification. The value of wavelet networks lies in their capabilities of extracting essential features in time-frequency plane. Furthermore, both the position and the dilation of the wavelets are optimized besides the weights of the network during the training phase. In this paper, the basic concept of wavelet-based feature extraction is firstly described. Then the theory of wavelet networks is introduced for the hyperspectral image classification. Finally an AVIRIS image was used to test the feasibility and performance of classification using the wavelet networks. The experiment results showed that the wavelet networks exactly an effective tool for classification of hyperspectral images, and have better classification results than the traditional feed-forward multi-layer neural networks.