Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral
Super-Resolution
- URL: http://arxiv.org/abs/2007.05230v3
- Date: Sat, 1 Aug 2020 12:48:53 GMT
- Title: Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral
Super-Resolution
- Authors: Jing Yao, Danfeng Hong, Jocelyn Chanussot, Deyu Meng, Xiaoxiang Zhu,
Zongben Xu
- Abstract summary: We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI.
Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
- Score: 79.97180849505294
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent advancement of deep learning techniques has made great progress on
hyperspectral image super-resolution (HSI-SR). Yet the development of
unsupervised deep networks remains challenging for this task. To this end, we
propose a novel coupled unmixing network with a cross-attention mechanism,
CUCaNet for short, to enhance the spatial resolution of HSI by means of
higher-spatial-resolution multispectral image (MSI). Inspired by coupled
spectral unmixing, a two-stream convolutional autoencoder framework is taken as
backbone to jointly decompose MS and HS data into a spectrally meaningful basis
and corresponding coefficients. CUCaNet is capable of adaptively learning
spectral and spatial response functions from HS-MS correspondences by enforcing
reasonable consistency assumptions on the networks. Moreover, a cross-attention
module is devised to yield more effective spatial-spectral information transfer
in networks. Extensive experiments are conducted on three widely-used HS-MS
datasets in comparison with state-of-the-art HSI-SR models, demonstrating the
superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes
and datasets will be available at:
https://github.com/danfenghong/ECCV2020_CUCaNet.
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