Research on supervised LPP feature extraction for hyperspectral image

For the classification among different land-cover types in a hyperspectral image, particularly in the small-sample-size problem, a feature extraction method is an approach for reducing the dimensionality and increasing the classification accuracy. A supervised principal locality preserving projection (SPLPP ) feature extraction algorithms, which uses the label information of training sample in locality preserving projection (LPP), was proposed in this paper. Three main steps are involved in the proposed SLPP: firstly uses PCA to remove redundant information, and then combines the label information in LPP, finally, SPLPP projects high-dimensional hyperspectral image into a low-dimensional space. Last but not least, SPLPP uses the extracted features as inputs of classifiers (e.g. support vector machine (SVM ) and K-nearest neighbors (KNN )) to do classification. Experimental results show that the proposed SPLPP has better local information retention ability and class discrimination ability compared with PCA, LPP, LD