Pan-sharpening via High-pass Modification Convolutional Neural Network

• URL: http://arxiv.org/abs/2105.11576v1
• Date: Mon, 24 May 2021 23:39:04 GMT
• Title: Pan-sharpening via High-pass Modification Convolutional Neural Network
• Authors: Jiaming Wang, Zhenfeng Shao, Xiao Huang, Tao Lu, Ruiqian Zhang, Jiayi Ma
• Abstract summary: We propose a novel pan-sharpening convolutional neural network based on a high-pass modification block. The proposed block is designed to learn the high-pass information, leading to enhance spatial information in each band of the multi-spectral-resolution images. Experiments demonstrate the superior performance of the proposed method compared to the state-of-the-art pan-sharpening methods.
• Score: 39.295436779920465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification block. Different from existing methods, the proposed block is designed to learn the high-pass information, leading to enhance spatial information in each band of the multi-spectral-resolution images. To facilitate the generation of visually appealing pan-sharpened images, we propose a perceptual loss function and further optimize the model based on high-level features in the near-infrared space. Experiments demonstrate the superior performance of the proposed method compared to the state-of-the-art pan-sharpening methods, both quantitatively and qualitatively. The proposed model is open-sourced at https://github.com/jiaming-wang/HMB.

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