Nonlinear graph-based dimensionality reduction algorithms have been shown to be very effective at yielding low-dimensional representations of hyperspectral image data. However, the steps of graph construction and eigenvector computation often suffer from prohibitive computational and memory requirements. In the paper, we develop a semi-supervised deep auto-encoder network (SSDAN) that is capable of generating mappings that approximate the embeddings computed by the nonlinear DR methods. The SSDAN is trained with only a small subset of the original data and enables an expert user to provide constraints that can bias data points from the same class towards being mapped closely together. Once the SSDAN is trained on a small subset of the data, it can be used to map the rest of the data to the lower dimensional space, without requiring complicated out-of-sample extension procedures that are often necessary in nonlinear DR methods. Experiments on publicly available hyperspectral imagery (Indian Pines and Salinas) demonstrate that SSDANs compute low-dimensional embeddings that yield good results when input to pixel-wise classification algorithms.
Xuewen Zhang and Nathan D. Cahill, "Semi-supervised deep autoencoder network for graph-based dimensionality reduction of hyperspectral imagery," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440K (Presented at SPIE Defense + Security: April 18, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2303912.
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