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2017 | OriginalPaper | Buchkapitel

Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution

verfasst von : Chen Wang, Yun Liu, Xiao Bai, Wenzhong Tang, Peng Lei, Jun Zhou

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Hyperspectral image is very useful for many computer vision tasks, however it is often difficult to obtain high-resolution hyperspectral images using existing hyperspectral imaging techniques. In this paper, we propose a deep residual convolutional neural network to increase the spatial resolution of hyperspectral image. Our network consists of 18 convolution layers and requires only one low-resolution hyperspectral image as input. The super-resolution is achieved by minimizing the difference between the estimated image and the ground truth high resolution image. Besides the mean square error between these two images, we introduce a loss function which calculates the angle between the estimated spectrum vector and the ground truth one to maintain the correctness of spectral reconstruction. In experiments on two public datasets we show that the proposed network delivers improved hyperspectral super-resolution result than several state-of-the-art methods.

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Vorheriges Kapitel Classification of Hyperspectral Imagery Based on Dictionary Learning and Extended Multi-attribute Profiles

Nächstes Kapitel Stereoscopic Digital Camouflage Pattern Generation Algorithm Based on Color Image Segmentation

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Titel: Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution
verfasst von: Chen Wang
Yun Liu
Xiao Bai
Wenzhong Tang
Peng Lei
Jun Zhou
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-71597-1

Electronic ISBN: 978-3-319-71598-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71598-8_33

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