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Proceedings Paper

Deep learning with convolutional fiber filters for spectral analysis of hyperspectral imagery (Conference Presentation)
Author(s): Robert S. Rand
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Paper Abstract

A deep learning convolutional network of fiber filters is investigated for the spectral analysis of hyperspectral imagery for purposes of material classification and identification. Analogous to convolutional neural networks that apply spatial filters to color imagery for purposes of spatial object classification, a network of convolutional filters is applied spectrally to the large numbers of bands in hyperspectral imagery for purposes of material classification and identification. A convolutional filter has a volume NxNxM, where N is the spatial pixel size (length and width) of the filter and M is the filter depth. For spatial convolutional networks, the filter applied to the first (bottom/input) layer is often N = 5 and M = 3 (e.g., corresponding to each layer of RGB color imagery). For the proposed network, the convolution filters in each layer have a volume with the dimension of N = 1 and M > 5, which we refer to as fiber filters. We investigate the ability of this kind of architecture to learn discriminating features for purposes of material classification and identification as compared to a fully connected neural network. The choice of appropriate architecture depth is investigated, which is the number of layers in the network, not to be confused with the filter depth. Various values the filter depth, M, are also investigated. Aerial collections of hyperspectral imagery are used for training and the validation experiment.

Paper Details

Date Published: 14 May 2018
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Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440J (14 May 2018); doi: 10.1117/12.2305291
Show Author Affiliations
Robert S. Rand, R3-Experts, Inc. (United States)


Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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