Hyperspectral data exhibit the characteristics of many bands and data redundancy. This study introduces the wavelet packet entropy feature in hyperspectral remote sensing classification. 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. 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. 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.