Manifold learning method can find the low-dimensional manifold structures embedded in high-dimensional data. However

the traditional manifold learning algorithms assume that all samples lie on a single manifold

while the samples in different subsets may belong to different sub-manifolds. To solve the above problem

a new dimensionality reduction (DR) method termed supervised multi-manifold discriminant embedding (SMMDE) is proposed for classification of hyperspectral remote sensing image. At first

the proposed method explore the labels of HSI data to divide samples into different sub-manifolds. Based on the graph embedding framework

the intra-manifold and inter-manifold graphs are constructed to represent the multi-manifold structure of HSI data

and the intra-class aggregation and inter-class separation are enhanced by minimizing the intra-manifold distance and maximizing the inter-manifold distance simultaneously. Therefore

low-dimensional discriminant features are obtained to improve the performance of HSI classification. Experimental results on the PaviaU and KSC hyperspectral data sets show that the overall classification accuracies respectively reach 88.04% and 84.53% when 2% training samples are randomly selected for training. The proposed SMMDE method can effectively improve classification performance compared with many state-of-art DR algorithms.