结合双非局部注意力感知的SAR和光学图像金字塔细节融合网络
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

安徽省重点研究与开发计划(202004a07020030); 中央高校基本科研业务费专项资金(JZ2021HGTB0111); 安徽省自然科学基金(2108085MF233)


Pyramid Detail Fusion Network for SAR and Optical Image Based on Dual Non-local Attention Perception
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    合成孔径雷达(synthetic aperture radar, SAR)与光学图像融合旨在利用卫星传感器的成像互补性, 生成更全面的地貌信息. 然而, 由于各单一卫星传感器数据分布的异质性和成像物理机制的差异, 现有网络模型在融合过程中往往存在成像精度低的问题. 为了解决上述问题, 本文提出DNAP-Fusion, 一种新的结合双非局部注意力感知的SAR和光学图像金字塔细节融合网络(dual non-local-aware-based pyramid fusion net). 该方法利用双非局部注意力模块, 在空间尺度逐渐减小的多级图像金字塔中提取SAR图像的结构信息和光学图像的纹理细节. 然后在空间和通道维度上融合它们的互补特征. 然后, 通过图像重构将融合特征注入上采样光学图像中, 得到最终的融合结果. 此外, 在网络训练之前, 采用图像封装决策来增强同一场景中SAR和光学图像中目标之间的共性关系. 定性和定量的实验结果表明, 提出的方法优于现有融合方法, 其中客观评价指标中的相关系数(correlation coefficient, CC)为0.990 6, 峰值信噪比(peak signal to noise ratio, PSNR)为32.156 0 dB. 此外, 所提方法有效地融合了SAR图像和可见光图像的互补特征, 为提高遥感图像融合的精度和有效性提供了一种有价值的思路和方法.

    Abstract:

    Synthetic aperture radar (SAR) and optical image fusion aim to leverage the imaging complementarity of satellite sensors for generating more comprehensive geomorphological information. However, existing network models often exhibit low imaging accuracy during the fusion process due to the heterogeneity in data distribution of each single satellite sensor and differences in imaging physical mechanisms. This study proposes the DNAP-Fusion, a novel SAR and optical image fusion network that incorporates dual non-local attention perception. The proposed method utilizes a dual non-local perceptual attention module to extract structural information from SAR images and texture details from optical images within a multi-level image pyramid with a gradually decreasing spatial scale. It then fuses their complementary features in both spatial and channel dimensions. Subsequently, the fused features are injected into the upsampled optical image through image reconstruction, resulting in the final fusion outcome. Additionally, before network training, image encapsulation decisions are employed to enhance the commonality between objects in SAR and optical images within the same scene. Qualitative and quantitative experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) multisensor fusion methods. The correlation coefficient (CC) in the objective evaluation indices is 0.990 6, and the peak signal to noise ratio (PSNR) is 32.156 0 dB. Moreover, the proposed method effectively fuses the complementary features of SAR and optical images, offering a valuable idea and method for enhancing the accuracy and effectiveness of remote sensing image fusion.

    参考文献
    相似文献
    引证文献
引用本文

朱佳佳,杨学志,梁宏博,杨翔宇.结合双非局部注意力感知的SAR和光学图像金字塔细节融合网络.计算机系统应用,,():1-11

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-12-15
  • 最后修改日期:2024-01-17
  • 录用日期:
  • 在线发布日期: 2024-06-28
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号