A review of pansharpening methods based on deep learning

doi:10.6046/zrzyyg.2021433

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Abstract

With the fast development and wide application of remote sensing technology, remote sensing images with higher quality are needed. However, it is difficult to directly acquire high-resolution, multispectral remote sensing images. To obtain high-quality images by integrating the information from different imaging sensors, pansharpening technology emerged. Pansharpening is an effective method used to obtain multispectral images with high spatial resolution. Many scholars have studied this method and obtained fruitful achievements. In recent years, deep learning theory has developed rapidly and has been widely applied in pansharpening. This study aims to systematically introduce the progress in pansharpening and promote its development. To this end, this study first introduced the traditional, classical pansharpening methods, followed by commonly used remote sensing satellites. Then, this study elaborated on the pansharpening methods based on deep learning from the perspective of supervised learning, unsupervised learning, and semi-supervised learning. After that, it described and analyzed loss functions. To demonstrate the superiority of the pansharpening methods based on deep learning and analyze the effects of loss functions, this study conducted remote sensing image fusion experiments. Finally, this study presented the future prospects of the pansharpening methods based on deep learning.

Keywords remote sensing image pansharpening deep learning convolutional neural network

ZTFLH:

TP391.4

Issue Date: 20 March 2023

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	Jianwen HU
	Zeping WANG
	Pei HU

Cite this article:

Jianwen HU,Zeping WANG,Pei HU. A review of pansharpening methods based on deep learning[J]. Remote Sensing for Natural Resources, 2023, 35(1): 1-14.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021433 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/1

Tab.1 Basic parameters of common satellites

Fig.1 Classification of pansharpening based on deep learning

Fig.2 Pansharpening based on supervised learning

Tab.2 Comparison among pansharpening methods based on supervised learning

Fig.3 Pansharpening based on generative dversarial network

Tab.3 Comparison of pansharpening methods

Tab.4 Performance comparison of different loss function

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