Extended random walkers for hyperspectral image classification

A novel spectral-spatial hyperspectral image classification is proposed based on extended random walkers. First, a widely used pixel-wise classifier, i.e., the support vector machine (SVM), is adopted to obtain probability maps for a hyper-psectral image, which measure the probabilities that a pixel belongs to different classes. Then, the initial probabilities are optimized with the extended random walkers. Finally, by assigning each pixel with the label for which the greatest probability is obtained, the classification result is obtained. Experiments show the outstanding performance of the proposed method in terms of classification accuracy especially when the number of training samples is relatively small.