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Self-Dictionary Regression for Hyperspectral Image Super-Resolution

1
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
The College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
3
The College of Sciences, Huazhong Agricultural University, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1574; https://doi.org/10.3390/rs10101574
Received: 28 June 2018 / Revised: 13 September 2018 / Accepted: 21 September 2018 / Published: 1 October 2018
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Abstract

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods. View Full-Text
Keywords: hyperspectral image super-resolution; data fusion; self-dictionary regression hyperspectral image super-resolution; data fusion; self-dictionary regression
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Gao, D.; Hu, Z.; Ye, R. Self-Dictionary Regression for Hyperspectral Image Super-Resolution. Remote Sens. 2018, 10, 1574.

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