目的 针对高光谱数据波段多、数据存在冗余的特点，将小波包信息熵特征引入到高光谱遥感分类中。方法 通过对光谱曲线进行小波包分解变换，定义了小波包信息熵特征矢量光谱角分类方法(WPE-SAM)，基于USGS光谱库中4种矿物光谱数据的分析表明，WPE-SAM可增大类间地物的可区分性。在特征矢量空间对Salina高光谱影像进行分类计算，并讨论了小波包最佳分解层的确定，分析了WPE-SAM与光谱角制图(SAM)方法的分类精度。结果 Salina数据实例计算表明：小波包信息熵矢量能较好地描述原始光谱特征，WPE-SAM分类方法可行，总体分类精度(OA)由SAM的78.62%提高到WPE-SAM的78.66%，Kappa系数由0.769 0增加到0.769 5，平均分类精度(AA)由83.14%提高到84.18%。此外，通过Pavia数据验证了WPE-SAM分类方法具有较强的普适性。结论 小波包信息熵特征可较好地表示原始光谱波峰、波谷等特征信息，定义的小波包信息熵特征矢量光谱角分类方法(WPE-SAM)可增大类间地物可区分性，有利于分类。实验结果表明，WPE-SAM分类方法技术可行，总体精度及Kappa系数较SAM有一定的提高，且有较强的普适性。但WPE-SAM方法精度与效率有待进一步提高。

Objective Hyperspectral data exhibit the characteristics of many bands and data redundancy. This study introduces the wavelet packet entropy feature in hyperspectral remote sensing classification. Method The new classification method of wavelet packet entropy feature vector angle (WPE-SAM)is defined based on WP coefficients, which are obtained by utilizing the optimal level WP decomposition of the spectral curve. An analysis of the WPE-SAM of four types of mineral spectra from the USFS library indicates that WPE-SAM can increase the distinction of different features. The Salina data is addressed by the WPE-SAM in feature space, the optimal deposition level is analyzed in the experiment, and the classification accuracy of WPE-SAM and SAM is also discussed. Result Experiment results show that the WPE feature has a better description of the original spectral feature. The WPE-SAM classification method is feasible, and the overall classification accuracy improved from 78.62% for SAM to 78.66% for WPE-SAM. The Kappa coefficient increased from 0.769 0 to 0.769 5, and the average classification accuracy from 83.14% to 84.18%. Classification results of the Pavia data also show that WPE-SAM has universal applicability. Conclusion The WPE feature has a good description of the original spectral feature, such as reflectance peak and absorption valley. WPE-SAM can also increase the distinction of different features. Experiment results show that the WPE-SAM classification method is feasible, the overall classification accuracy and Kappa coefficients of WPE-SAM are higher than those of SAM, and WPE-SAM exhibits strong universal applicability. The accuracy and efficiency of WPE-SAM should be further improved.