CSAFNet: Channel Similarity Attention Fusion Network for Multispectral Pansharpening

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Luo, Shuyue, Zhou, Shangbo, Qi, Ying
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel attention; Convolution; Convolutional neural network (CNN); Correlation; Data fusion; Feature extraction; Multispectral (MS) pansharpening; Neural networks; Resolution; Sensors; Similarity; Spatial resolution; Tensors; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3040893

Multispectral (MS) pansharpening involves fusing a low-spatial-resolution MS image and its associated high-spatial-resolution panchromatic image. Recently, convolutional neural network (CNN)-based fusion models have been widely used in pansharpening domain, but most of them treat diversity features equally and also neglect the contribution of multilevel features, thereby impeding the representation ability of CNNs. To deal with these issues, we propose a novel channel similarity attention fusion network (CSAFNet) in this letter, where several channel attention residual dense blocks (CARDBs) are stacked to fully exploit discriminative features, and then the features produced by all CARDBs are combined via a multilevel feature fusion module. Such network enables the network to focus on more informative features and make full use of them. Both visual and quantitative assessments validate the superior performance of the proposed network over the current pansharpening methods with respect to spectral fidelity and spatial enhancement.