人们在处理高光谱图像时一般要对一些典型地物进行光谱分析、特征波段的提取，以便提取出最大量的有效信息，剔除无用或冗余的信息，然后再进行分类识别。采用小波变换的分析方法，选用合适的小波进行分解，根据分解后的高频分量中包含的重要信息，利用局部相邻的正负极值点找出对应于原始光谱曲线上每个吸收带的左右边界；利用局部过零点，即可比较精确的提取出各个吸收带的中心波长。该方法比传统的光谱特征提取方法更简洁、有效，实验证明为一种比较理想的光谱特征提取方法。

The high spectral resolution of hyper-spectral data provides the ability for diagnostic identification of various materials. The purpose of spectral feature extraction is to extract substantial information from the original data input and filter out redundant information,then classification and identification can be processed. The author introduced a method based on wavelet decomposition for spectral feature extraction. Appropriate wavelet is applied to the pre-analyzing signals and make it decomposed,then the high-frequency component contains much important feature information. We can detect every absorption strips and their left and right boundaries according to the original signal by finding the local modulus maxima of high-frequency weight. The method is terser and more effective than other traditional methods. It proved that wavelet analysis is a more perfect,method of spectral feature extraction.