HPGAN: Hyperspectral Pansharpening Using 3-D Generative Adversarial Networks

Weiying Xie; Yuhang Cui; Yunsong LiJie LeiQian DuJiaojiao Li

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HPGAN: Hyperspectral Pansharpening Using 3-D Generative Adversarial Networks

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Hyperspectral (HS) pansharpening, as a special case of the superresolution (SR) problem, is to obtain a high-resolution (HR) image from the fusion of an HR panchromatic (PAN) image and a low-resolution (LR) HS image. Though HS pansharpening based on deep learning has gained rapid development in recent years, it is still a challenging task because of the following requirements: 1) a unique model with the goal of fusing two images with different dimensions should enhance spatial resolution while preserving spectral information; 2) all the parameters should be adaptively trained without manual adjustment; and 3) a model with good generalization should overcome the sensitivity to different sensor data in reasonable computational complexity. To meet such requirements, we propose a unique HS pansharpening framework based on a 3-D generative adversarial network (HPGAN) in this article. The HPGAN induces the 3-D spectral–spatial generator network to reconstruct the HR HS image from the newly constructed 3-D PAN cube and the LR HS image. It searches for an optimal HR HS image by successive adversarial learning to fool the introduced PAN discriminator network. The loss function is specifically designed to comprehensively consider global constraint, spectral constraint, and spatial constraint. Besides, the proposed 3-D training in the high-frequency domain reduces the sensitivity to different sensor data and extends the generalization of HPGAN. Experimental results on data sets captured by different sensors illustrate that the proposed method can successfully enhance spatial resolution and preserve spectral information.

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《IEEE Transactions on Geoscience and Remote Sensing》 |2021年第1期|463-477|共15页
作者
Weiying Xie; Yuhang Cui; Yunsong LiJie LeiQian DuJiaojiao Li;
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作者单位

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China;

Department of Electronic and Computer Engineering, Mississippi State University, Starkville, MS, USA;

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正文语种英语
中图分类地球物理学;遥感技术;
关键词
Generators; Generative adversarial networks; Spatial resolution; Gallium nitride; Bayes methods; Data models; global constraint; hyperspectral pansharpening; spectral–spatial constraints; 3-D high-frequency block;

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HPGAN: Hyperspectral Pansharpening Using 3-D Generative Adversarial Networks

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