提出了一种新颖的用于高光谱遥感图像特征提取的子波变换算法。与二进小波变换按恒Q准则划分频域不同的是，该算法通过改变相邻子波的带宽比，可以实现更为灵活的频域划分。采用子波能量的离散余弦变换作为特征矢量，然后进行无监督C均值聚类实验和有监督RBF（径向基函数）神经网络分类实验。实验结果表明，子波变换能量的离散余弦变换特征可以有效地描述光谱曲线特征，且正确分类率高于传统的小波变换。

A new feature extraction method for remote sensing image was proposed based on a novel wavelet transform algorithm. Different from binary wavelet transform partition 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 the traditional wavelet transform algorithm.