Semi-supervised deep autoencoder network for graph-based dimensionality reduction of hyperspectral imagery

Xuewen Zhang; Nathan D. Cahill

doi:10.1117/12.2303912

8 May 2018 Semi-supervised deep autoencoder network for graph-based dimensionality reduction of hyperspectral imagery

Xuewen Zhang, Nathan D. Cahill

Proceedings Volume 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV; 106440K (2018) https://doi.org/10.1117/12.2303912
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (8 May 2018); https://doi.org/10.1117/12.2303912

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available