We study the transfer learning behavior of a Hybrid Deep Network (HDN) applied to a challenging longwave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower, over multiple full diurnal cycles and different atmospheric conditions. The HDN architecture adopted in this study stakes a number of Restricted Boltzmann Machines to form a deep belief network for generative pre-training, or initialization of weight parameters, and then combines with a discriminative learning procedure that fine-tune all of the weights jointly to improve the network’s performance. 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 of significant data variability observed between and within classes due to environmental and temperature variation, occurring within full diurnal cycles. We argue, however, that more question are raised than answers are provided regarding the generalization capacity of these deep nets through experiments aimed for investigating their training and transfer learning behavior in the longwave infrared region of the electromagnetic spectrum.
Dalton Rosario and Patrick Rauss, "Deep learning over diurnal and other environmental effects," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980E (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262866.
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