Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation

Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability in a variety of fields. Deep learning...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Nalepa, Jakub, Tulczyjew, Lukasz, Myller, Michal, Kawulok, Michal
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Data augmentation
Hyperspectral imaging
Machine learning
Neural networks
Spectra
Training
Online Access:Get full text
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Summary:Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability in a variety of fields. Deep learning has established the state of the art in the area, and it constitutes the current research mainstream. In this letter, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our rigorous experiments showed that the proposed method outperforms other spectral-spatial techniques from the literature, and delivers precise hyperspectral classification in real time.
ISSN:2331-8422