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Remote Sens. 2017, 9(11), 1196;

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
Author to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 15 November 2017 / Accepted: 16 November 2017 / Published: 21 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
PDF [7247 KB, uploaded 21 November 2017]


Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution. View Full-Text
Keywords: hyperspectral imaging; super resolution; spectral unmixing; relative spatial response; relative spectral response; data fusion hyperspectral imaging; super resolution; spectral unmixing; relative spatial response; relative spectral response; data fusion

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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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Lanaras, C.; Baltsavias, E.; Schindler, K. Hyperspectral Super-Resolution with Spectral Unmixing Constraints. Remote Sens. 2017, 9, 1196.

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