Jiang, Ruituo, Li, Xu, Gao, Ang et al, Li, Lixin, Meng, Hongying, Yue, Shigang and Zhang, Lei
(2019)
Learning spectral and spatial features based on generative adversarial network for hyperspectral image super-resolution.
In: The 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS2019).
Full content URL: https://doi.org/10.1109/IGARSS.2019.8900228
| Learning spectral and spatial features based on generative adversarial network for hyperspectral image super-resolution | | Accepted Manuscript | | ![[img]](http://eprints.lincoln.ac.uk/42331/1.hassmallThumbnailVersion/20190512%20IGARSS2019%20Paper_1598%20SV.pdf)
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| Item Type: | Conference or Workshop contribution (Paper) |
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| Item Status: | Live Archive |
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Abstract
Super-resolution (SR) of hyperspectral images (HSIs) aims to enhance the spatial/spectral resolution of hyperspectral imagery and the super-resolved results will benefit many remote sensing applications. A generative adversarial network for HSIs super-resolution (HSRGAN) is proposed in this paper. Specifically, HSRGAN constructs spectral and spatial blocks with residual network in generator to effectively learn spectral and spatial features from HSIs. Furthermore, a new loss function which combines the pixel-wise loss and adversarial loss together is designed to guide the generator to recover images approximating the original HSIs and with finer texture details. Quantitative and qualitative results demonstrate that the proposed HSRGAN is superior to the state of the art methods like SRCNN and SRGAN for HSIs spatial SR.
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