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Spectral Super-Resolution with Optimized Bands

Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Boeing Research and Technology, Huntsville, AL 35824, USA
Department of Electrical & Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1648;
Received: 7 May 2019 / Revised: 24 June 2019 / Accepted: 5 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Multispectral Image Acquisition, Processing and Analysis)
PDF [35367 KB, uploaded 11 July 2019]


Hyperspectral (HS) sensors sample reflectance spectrum in very high resolution, which allows us to examine material properties in very fine details. However, their widespread adoption has been hindered because they are very expensive. Reflectance spectra of real materials are high dimensional but sparse signals. By utilizing prior information about the statistics of real HS spectra, many previous studies have reconstructed HS spectra from multispectral (MS) signals (which can be obtained from cheaper, lower spectral resolution sensors). However, most of these techniques assume that the MS bands are known apriori and do not optimize the MS bands to produce more accurate reconstructions. In this paper, we propose a new end-to-end fully convolutional residual neural network architecture that simultaneously learns both the MS bands and the transformation to reconstruct HS spectra from MS signals by analyzing large quantity of HS data. The learned band can be implemented in hardware to obtain an MS sensor that collects data that is best to reconstruct HS spectra using the learned transformation. Using a diverse set of real-world datasets, we show how the proposed approach of optimizing MS bands along with the transformation can drastically increase the reconstruction accuracy. Additionally, we also investigate the prospects of using reconstructed HS spectra for land cover classification. View Full-Text
Keywords: hyperspectral spectra; multispectral signals; spectral super-resolution; optimized bands; convolutional neural networks hyperspectral spectra; multispectral signals; spectral super-resolution; optimized bands; convolutional neural networks

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gewali, U.B.; Monteiro, S.T.; Saber, E. Spectral Super-Resolution with Optimized Bands. Remote Sens. 2019, 11, 1648.

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