Abstract: Aiming to the problem that the original manifold learning algorithm only utilizes spectral characteristics without incorporating spatial information, a locality preserving projections algorithm based on spatial-spectral (SS-LPP: Spatial-Spectral Locality Preserving Projections) union is proposed. Firstly, the weighted mean filtering algorithm is used to filter the dataset, fuse the spatial information with the spectral information, and eliminate the interference of noise, to increase the smoothness of similar data. Then, the label set is used to construct intra-graph and inter-graph. Through the intra-graph and inter-graph, identification features can be effectively extracted, and the classification performance can be improved. The effectiveness of the algorithm is verified on the Salinas dataset and the PaviaU dataset. Experimental results show that the algorithm can effectively extract data features and improve the accuracy of classification.

Key words: manifold learning, dimensionality reduction, hyperspectral remote sensing, feature extraction