Deep Residual Learning for Boosting the Accuracy of Hyperspectral Pansharpening

Recently, deep learning (DL) has gained impressive achievements in the field of remote sensing image fusion. However, most of the previous DL-based fusion methods are originally designed for multispectral pansharpening, which cannot be readily employed to hyperspectral pansharpening due to the much...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-08, Vol.17 (8), p.1435-1439
Main Authors: Zheng, Yuxuan, Li, Jiaojiao, Li, Yunsong, Cao, Kailang, Wang, Keyan
Format: Article
Language:English
Subjects:
Accuracy
Adaptive filters
Artificial neural networks
Computer vision
Contrast limited adaptive histogram equalization (CLAHE)
deep residual convolutional neural network (DRCNN)
Equalization
Frameworks
guided filter
Histograms
Hyperspectral imaging
hyperspectral pansharpening
Image edge detection
Image processing
Machine learning
Methods
Neural networks
Remote sensing
Resolution
Spatial discrimination
Spatial resolution
Training
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Summary:Recently, deep learning (DL) has gained impressive achievements in the field of remote sensing image fusion. However, most of the previous DL-based fusion methods are originally designed for multispectral pansharpening, which cannot be readily employed to hyperspectral pansharpening due to the much wider spectral range and lower spatial resolution of a hyperspectral image (HSI). In this letter, a novel framework based on deep residual learning is proposed for hyperspectral pansharpening. The proposed framework consists mainly of two parts. First, the initialized HSI with the enhanced spatial resolution is generated through contrast limited adaptive histogram equalization (CLAHE) and guided filter. Then, a deep residual convolutional neural network (DRCNN) is introduced to map the residuals between the initialized HSI and the reference HSI for further boosting the fusion accuracy. Experimental results demonstrate that the proposed framework can achieve superior performance compared with the existing state-of-the-art pansharpening methods, especially in terms of edge details enhancement.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2945424