Hyperspectral Image Classification With Data Augmentation and Classifier Fusion

Recently, deep convolutional neural network (DCNN)-based methods have achieved much success in hyperspectral image (HSI) classification, when sufficient labeled samples are provided during training. However, due to the expensive cost of labeling in HSIs, only limited labeled samples can be given in practice, which often causes these methods to be overfitting. To address this problem, we present a new HSI classification method in this study, which is constructed in the following two steps. First, we establish a data mixture model to augment the labeled training set quadratically and train a DCNN-based classifier on it. Then, through randomly sampling the coefficient in the data mixture model, we obtain several independent classifiers and fuse them with a voting strategy to produce the final classification results. Since both data augmentation and classifier fusion are effective to deal with limited samples, the proposed method shows superior performance in the classification of HSIs, which can be demonstrated by the experimental results on two benchmark HSI data sets.