In this article, we propose a surface- and deep-level constraint-based pan-sharpening network, termed SDPNet, to address the pan-sharpening problem. Focusing on the two primary goals of pan-sharpening, i.e., spatial and spectral information preservations, we first design two encoder–decoder networks to extract deep-level features from two types of source images, in addition to surface-level characteristics, as the enhanced information representation. The unique feature maps that characterize the unique information in source images can be obtained through the deep-level feature extraction. We further design a pan-sharpening network with densely connected blocks to strengthen feature propagation and reduce parameter number, where the unique feature maps are utilized to efficiently constrain the similarity between the pan-sharpened result and the ground truth, thus avoiding information distortion. Both qualitative and quantitative comparisons on the reduced-resolution and full-resolution source images demonstrate the advantages of our method over state-of-the-art methods. Our code is publicly available at https://github.com/hanna-xu/SDPNet .

中文翻译：

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SDPNet：具有增强的信息表示功能的深层网络

在本文中，我们提出了一种基于表面和深层约束的泛锐化网络，称为SDPNet，以解决泛锐化问题。针对泛锐化的两个主要目标，即空间和光谱信息的保存，我们首先设计了两个编码器-解码器网络，以从两种类型的源图像中提取深层特征，此外还包括表面层特征。增强的信息表示能力。可以通过深层特征提取来获得表征源图像中唯一信息的唯一特征图。我们进一步设计了具有紧密连接的图块的泛锐化网络，以增强特征传播并减少参数数量，利用独特的特征图来有效地限制泛锐化结果和地面真实性之间的相似性，从而避免信息失真。在降低分辨率和全分辨率源图像上的定性和定量比较都证明了我们的方法优于最新方法的优势。我们的代码可在以下位置公开获得https://github.com/hanna-xu/SDPNet 。