Deep Manifold Reconstruction Neural Network for Hyperspectral Image Classification

Deep learning (DL) has received extensive attention from the remote sensing community in recent years due to its ability to learn deep abstract information through a hierarchical network. However, most DL methods fail to explore the local geometric structure relationship between samples within hyperspectral imagery (HSI) to improve feature extraction performance. To address this issue, a novel DL approach, termed deep manifold reconstruction neural network (DMRNet), is proposed in this letter. By introducing a graph embedding framework, DMRNet calculates a reconstruction point of each sample with corresponding neighbors and then constructs a graph model to discover the intrinsic manifold structure in HSI. On this basis, DMRNet develops a joint loss function to reduce the difference between actual and predictive values, and to explore the separability of the extracted deep features. Experimental results on real-world HSI data sets exhibit the superiority of DMRNet to some state-of-the-art methods.