AWFLN: An Adaptive Weighted Feature Learning Network for Pansharpening

Deep learning (DL)-based pansharpening methods have shown great advantages in extracting spectral-spatial features from multispectral (MS) and panchromatic (PAN) images compared with traditional methods. However, most DL-based methods ignore the local inner connection between the source images and h...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Lu, Hangyuan, Yang, Yong, Huang, Shuying, Chen, Xiaolong, Chi, Biwei, Liu, Aizhu, Tu, Wei
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
adaptive feature learning
Convolution
Data mining
Deep learning
Feature extraction
Image resolution
Information processing
lightweight network
Methods
Pansharpening
Representation learning
Spatial data
Spatial resolution
spectral-spatial fidelity priori
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Summary:Deep learning (DL)-based pansharpening methods have shown great advantages in extracting spectral-spatial features from multispectral (MS) and panchromatic (PAN) images compared with traditional methods. However, most DL-based methods ignore the local inner connection between the source images and high-resolution MS (HRMS) image, which cannot fully extract spectral-spatial information, and attempt to improve the quality of fusion by increasing the complexity of the network. To solve these problems, a lightweight network based on adaptive weighted feature learning (AWFLN) is proposed for pansharpening. Specifically, a novel detail extraction model is first built by exploring the local relationship between HRMS and source images, thereby improving the accuracy of details and the interpretability of the network. Guided by this model, we then design a residual multiple receptive-field structure to fully extract spectral-spatial features of source images. In this structure, an adaptive feature learning block based on spectral-spatial interleaving attention is proposed to adaptively learn the weights of features and improve the accuracy of the extracted details. Finally, the pansharpened result is obtained by a detail injection model in AWFLN. Numerous experiments are carried out to validate the effectiveness of the proposed method. Compared to traditional and state-of-the-art methods, AWFLN performs the best both subjectively and objectively, with high efficiency. The code is available at https://github.com/yotick/AWFLN.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3241643