Deep greedy learning under thermal variability in full diurnal cycles

Patrick Rauss; Dalton Rosario

doi:10.1117/1.OE.56.8.081809

19 June 2017 Deep greedy learning under thermal variability in full diurnal cycles

Patrick Rauss, Dalton Rosario

Author Affiliations +

Optical Engineering, Vol. 56, Issue 8, 081809 (June 2017). https://doi.org/10.1117/1.OE.56.8.081809

Abstract

We study the generalization and scalability behavior of a deep belief network (DBN) applied to a challenging long-wave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower. The collections cover multiple full diurnal cycles and include different atmospheric conditions. Using complementary priors, a DBN uses a greedy algorithm that can learn deep, directed belief networks one layer at a time and has two layers form to provide undirected associative memory. The greedy algorithm initializes a slower learning procedure, which fine-tunes the weights, using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of spectral data and their labels, despite significant data variability between and within classes due to environmental and temperature variation occurring within and between full diurnal cycles. We argue, however, that more questions than answers are raised regarding the generalization capacity of these deep nets through experiments aimed at investigating their training and augmented learning behavior.

Citation Download Citation

Patrick Rauss and Dalton Rosario "Deep greedy learning under thermal variability in full diurnal cycles," Optical Engineering 56(8), 081809 (19 June 2017). https://doi.org/10.1117/1.OE.56.8.081809

Received: 4 February 2017; Accepted: 31 May 2017; Published: 19 June 2017

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