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Open AccessArticle

Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

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State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
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School of Electronics Information and Communications, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
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School of Computer Science, Wuhan University, Wuhan 430072, China
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Hongyi Honor College, Wuhan University, Wuhan 430072, China
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1009; https://doi.org/10.3390/rs12061009
Received: 12 February 2020 / Revised: 14 March 2020 / Accepted: 15 March 2020 / Published: 21 March 2020
(This article belongs to the Section Remote Sensing Image Processing)
Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually. View Full-Text
Keywords: hyperspectral image; multispectral image; remote sensing; temporal difference; spectral unmixing; endmember spatial information hyperspectral image; multispectral image; remote sensing; temporal difference; spectral unmixing; endmember spatial information
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Feng, X.; He, L.; Cheng, Q.; Long, X.; Yuan, Y. Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information. Remote Sens. 2020, 12, 1009.

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