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Proceedings Paper

Blended learning for hyperspectral data
Author(s): Ilya Kavalerov; Wojciech Czaja
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Paper Abstract

We explore the spectral spatial representation capabilities of convolutional neural networks for the purpose of classification of hyperspectral images. We examine several types of neural networks, including a novel technique that blends the Fourier scattering transform with a convolutional neural network. This method is naturally suited for the representation of hyperspectral data because it decomposes signals into multi-frequency bands, removing small perturbations such as noise, while also having the capability of neural networks to learn a hierarchical representation. We test our proposed method on the standard Pavia University hyperspectral dataset and demonstrate a new training set sampling strategy that reveals the inherent spatial bias present in some purely neural network methods. The results indicate that our form of blended learning is more effective at representing spectral data and less prone to overfitting the artificial spatial bias in hyperspectral data.

Paper Details

Date Published: 14 May 2019
PDF: 12 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861B (14 May 2019); doi: 10.1117/12.2519977
Show Author Affiliations
Ilya Kavalerov, Univ. of Maryland, College Park (United States)
Wojciech Czaja, Univ. of Maryland, College Park (United States)

Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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