Journal of Frontiers of Computer Science and Technology ›› 2024, Vol. 18 ›› Issue (5): 1271-1285.DOI: 10.3778/j.issn.1673-9418.2303044

• Graphics·Image • Previous Articles     Next Articles

Super-Resolution Reconstruction Algorithm of Remote Sensing Image with Two-Branch Semantic Enhanced Perception

WANG Chaoxue, DAI Ning   

  1. College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
  • Online:2024-05-01 Published:2024-04-29

融合双分支语义增强感知的遥感图像超分辨率重建算法

王超学,代宁   

  1. 西安建筑科技大学 信息与控制工程学院,西安 710055

Abstract: Aiming at the problem of poor reconstruction due to the blurring of feature targets in remote sensing images and the influence of background noise, in this paper, a super-resolution reconstruction algorithm for remote sensing images incorporating two-branch semantic enhanced perception is proposed.  Firstly, a global-local spatial attention module is designed to enhance the semantic representation of features at different scales of spatial global-local, and at the same time strengthen the discriminative ability of the network for effective feature groups. Secondly, a channel grouping-aggregation attention module is proposed to enhance the model’s discriminative ability for ground objects features by designing feature grouping-aggregation and channel attention modules, and strengthen the model’s ability to focus on effective feature channels. Experiments show that on the UC Merced dataset, the PSNR reaches 34.397 dB, 29.920 dB and 28.128 dB respectively at the ×2/×3/×4 multiplier, and the structural similarity reaches 0.931, 0.834 and 0.791 at the ×2/×3/×4 multiplier. On the AID dataset, the PSNR reaches 32.524 dB, 29.317 dB and 27.522 dB respectively at the×2/×3/×4 multiplier, and the structural similarity reaches 0.895, 0.829 and 0.721 at the ×2/×3/×4 multiplier. Compared with other mainstream algorithms, both indices are improved, and the edge and regional details of reconstructed images are better, effectively overcoming the problems of fuzzy feature information of ground objects and background noise, which lead to poor reconstruction effect of remote sensing images.

Key words: remote sensing image, spatial attention, channel attention, super-resolution reconstruction

摘要: 针对遥感图像中地物目标的特征信息模糊以及背景噪声影响导致遥感图像重建效果差的问题,提出一种融合双分支语义增强感知的遥感图像超分辨率重建算法。首先,设计了一种全局-局部空间注意力模块,该模块用于增强特征在空间全局-局部不同尺度下的语义表征能力,同时强化网络对有效特征组的分辨能力;其次,提出一种通道分组-聚合注意力模块,通过设计特征分组-聚合以及通道注意力模块,增强模型对地物目标特征的区分,强化对有效特征通道的关注能力。实验表明,所提算法在UC Merced数据集上,峰值信噪比在×2/×3/×4倍率下分别达到了34.397 dB、29.920 dB和28.128 dB,结构相似度在×2/×3/×4倍率下达到了0.931、0.834和0.791。在AID数据集上,峰值信噪比在×2/×3/×4倍率下分别达到了32.524 dB、29.317 dB和27.522 dB,结构相似度在×2/×3/×4倍率下达到了0.895、0.829和0.721。两个指标相较于等主流算法均有所提升,重建后图像的边缘与区域细节效果更优,有效克服了地物目标的特征信息模糊及背景噪声影响导致遥感图像重建效果差的问题。

关键词: 遥感图像, 空间注意力, 通道注意力, 超分辨率重建