In order to solve the problem of insufficient classification types and low classification accuracy using traditional discrete LiDAR, in this paper, the waveform features of Full-waveform LiDAR were analyzed and corrected to be used for land covers classification. Firstly, the waveforms were processed, including waveform preprocessing, waveform decomposition and features extraction. The extracted features were distance, amplitude, waveform width and the backscattering cross-section. In order to decrease the differences of features of the same land cover type and further improve the effectiveness of the features for land covers classification, this paper has made comprehensive correction on the extracted features. The features of waveforms obtained in Zhangye were extracted and corrected. It showed that the variance of corrected features can be reduced by about 20% compared to original features. Then classification ability of corrected features was clearly analyzed using the measured waveform data with different characteristics. To further verify whether the corrected features can improve the classification accuracy, this paper has respectively classified typical land covers based on original features and corrected features. Since the features have independently Gaussian distribution, the Gaussian mixture density model (GMDM) was put forward to be the classification model to classify the targets as road, trees, buildings and farmland in this paper. The classification results of these four land cover types were obtained according to the ground truth information gotten from CCD image data of the targets region. It showed that the classification accuracy can be improved by about 8% when the corrected features were used.