Abstract:
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 hier...Show MoreMetadata
Abstract:
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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 19)