Radiation information is essential to land cover classification, but general deep convolutional neural networks (DCNNs) hardly use this to advantage. Additionally, the limited amount of available remote sensing data restricts the efficiency of DCNN models though this can be overcome by data augmentation. However, normal data augmentation methods, which only involve operations such as rotation and translation, have little effect on radiation information. These methods ignore the rich information contained in the image data. In this article, the authors propose a feasible feature-based data augmentation method, which extracts spectral features that can reflect radiation information as well as geometric and texture features that can reflect image information prior to augmentation. Through feature extraction, this method indirectly enhances radiation information and increases the utilization of image information. Classification accuracies show an improvement from 80.20% to 89.20%, which further verifies the effectiveness of this method.