Supervised Locally Linear Embedding based dimension reduction for hyperspectral image classification

The nonlinear characteristics in hyperspectral data is considered as an influential factor curtailing the classification accuracy. To deal with the problem, a new method for classification is developed, especially for hyperspectral imagery (HSI). It is a supervised method based on Locally Linear Embedding (LLE) and k-Nearest Neighbor (KNN), named with KNN based supervised LLE (S-LLE KNN). We use two real HIS dataset of AVIRIS in experiment section and compare overall classification accuracy and accuracy of each class in different methods, the results shows that the supervised nonlinear feature extraction method contributes more to classification accuracies methods.