A Novel Wavelet Transform Algorithm for Feature Extraction of Hyperspectral Remote Sensing Image

A new feature extraction method of remote sensing image was proposed based on a novel wavelet transform algorithm. Different form binary wavelet transform partitions the frequency domain by constant Q criteria, the method can partition the frequency domain freely, through setting the ratio of bandwidth of adjacent wavelet. Feature extraction based on discrete cosine transform of the wavelet energy was performed. The results of C-means clustering and RBF neural networks classification experiments show that, the proposed feature of wavelet transform can effectively describe spectral curve, and has better classification rate than traditional wavelet transform.