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<p>Remote sensing super-resolution remains a research hotspot in the field of remote sensing. Unlike natural images, remote sensing images typically possess rich scenes and complex spatial structures, making it challenging to enhance their resolution. This paper introduces a hybrid attention module that integrates CNNs, Transformers, and spatial attention to effectively extract multi-level features from remote sensing images. The module seamlessly integrates the early feature projection processes of CNNs and Transformers, harmoniously blending CNNs' strength in local modeling with Transformers' expertise in global modeling, thereby simplifying the complexity of fusion. To accurately preserve the intricate details of the input images, we construct a structure enhancement module that focuses on extracting edge and linear structural details to retain the complex features of the images. Furthermore, we build a multi-level remote sensing super-resolution network. A series of experiments conducted on the AID dataset demonstrate its excellent effectiveness and generalization capabilities.</p>

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Multi-stage remote sensing super-resolution network with deep fusion and structure enhancement based on CNN and transformer

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Jingyi Liu,
  • Xiaomin Yang

摘要

Remote sensing super-resolution remains a research hotspot in the field of remote sensing. Unlike natural images, remote sensing images typically possess rich scenes and complex spatial structures, making it challenging to enhance their resolution. This paper introduces a hybrid attention module that integrates CNNs, Transformers, and spatial attention to effectively extract multi-level features from remote sensing images. The module seamlessly integrates the early feature projection processes of CNNs and Transformers, harmoniously blending CNNs' strength in local modeling with Transformers' expertise in global modeling, thereby simplifying the complexity of fusion. To accurately preserve the intricate details of the input images, we construct a structure enhancement module that focuses on extracting edge and linear structural details to retain the complex features of the images. Furthermore, we build a multi-level remote sensing super-resolution network. A series of experiments conducted on the AID dataset demonstrate its excellent effectiveness and generalization capabilities.