Spectral-spatial hyperspectral image classification based on extended training set

Hyperspectral remote sensing image classification achieved good effect using support vector machine (SVM) even with very few training samples. But due to restrictions on the number of samples, it is hard to further enhance classification accuracy when only using spectral information. On the other hand, one can improve the classification accuracy by increasing the training samples when the training samples are few. Accordingly, we present a method of extending the training samples by using spatial information. In this method, the classes of samples contained in one segmentation region are treated as the same class and the class labels of all the pixels in this region are decided by the class labels of the training samples contained in it. These new samples are then named as the extended training set. Experiments show that the proposed method in this paper has better effect than the direct use of majority voting method.