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2025 , Vol. 42 >Issue 6: 769 - 780

DOI: https://doi.org/10.7523/j.ucas.2024.007

电子信息与计算机科学

基于双时相特征的SAR生成光学影像方法

  • 翁永椿 ,
  • 马勇 ,
  • 陈甫 ,
  • 尚二萍 ,
  • 姚武韬 ,
  • 仉淑艳 ,
  • 杨进 ,
  • 刘建波
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  • 1 中国科学院空天信息创新研究院, 北京 100094;
    2 中国科学院大学, 北京 100049

收稿日期: 2023-02-13

  修回日期: 2024-03-04

  网络出版日期: 2024-04-03

基金资助

国家自然科学基金(42201063)、海南省重点研发计划(ZDYF2021SHFZ260)和海南自然科学青年基金(520QN295)资助

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A method for SAR-to-optical image synthesis based on bi-temporal features

  • WENG Yongchun ,
  • MA Yong ,
  • CHEN Fu ,
  • SHANG Erping ,
  • YAO Wutao ,
  • ZHANG Shuyan ,
  • YANG Jin ,
  • LIU Jianbo
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  • 1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2023-02-13

  Revised date: 2024-03-04

  Online published: 2024-04-03

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摘要

稳定连续的遥感光学影像时间序列应用价值巨大，但很多地区受云雨影响难以获取到这样的时序光学影像。利用合成孔径雷达(SAR)全天时、全天候成像能力，由SAR影像生成光学影像，能有效解决光学影像缺失问题，但目前复杂场景下的生成质量与简单场景相比差距显著。基于哨兵卫星影像构建不同场景的双时相数据集，改进条件对抗网络生成器，以提取和融合双时相SAR特征和辅助光学特征，同时采用一种策略来平衡SAR和光学特征权重。与其他方法相比，所提出的模型Fréchet inception距离(FID)和峰值信噪比(PSNR)指标最佳，复杂场景和简单场景下生成质量差距明显缩小。消融实验表明，所提出的模型与基准模型相比，FID下降46，PSNR提升6.6dB，SSIM提升0.44。该方法有效提升了不同场景下光学影像的生成质量。

关键词： 可见光影像生成; 双时相数据集; 合成孔径雷达; 生成对抗网络

本文引用格式

翁永椿 , 马勇 , 陈甫 , 尚二萍 , 姚武韬 , 仉淑艳 , 杨进 , 刘建波 . 基于双时相特征的SAR生成光学影像方法[J]. 中国科学院大学学报, 2025 , 42(6) : 769 -780 . DOI: 10.7523/j.ucas.2024.007

Abstract

The robust optical image time series are of great value in many applications of remote sensing. However, due to the effects of weather conditions like clouds and rain, it is very difficult to obtain such robust time series of optical images in many regions. Using the all-weather imaging capability of synthetic aperture radar (SAR) to generate optical images from SAR images is an effective solution to the missing data of optical images. But there is still a problem that the quality of generated images in complicated scenarios is much worse than that in simple scenarios. In this paper, we build bi-temporal datasets of different scenarios based on Sentinel imagery and propose an improved generator of conditional generation adversarial network. The encoder-decoder-based generator learns to extract and fuse the bi-temporal polarized SAR features and the additional optical features from the source time phase. In addition, a strategy to balance the weights of SAR and optical features is adopted. Comparison experiments show that our method is the best on FID and PSNR among all evaluated methods. The proposed method significantly reduces the gap in the quality of generated images between simple scenario and complicated scenario. The ablation study shows that our method outperforms the baseline model by 46 in FID, 6.6dB in PSNR and 0.44 in SSIM. Our method efficiently improves the quality of generated images in different scenarios.

Key words： optical image synthesis; bi-temporal dataset; SAR; generative adversarial network

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