Huang Dengshan, Tang Yulin, Wang Qisheng
College of Civil Engineering, Xiangtan University, Xiangtan 411105, China.
Sensors (Basel). 2022 Sep 18;22(18):7055. doi: 10.3390/s22187055.
Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetration. Limited by its working principles, SAR images are not easily interpreted, and fusing SAR images with optical multispectral images is a good solution to improve the interpretability of SAR images. This paper presents a novel image fusion method based on non-subsampled shearlet transform and activity measure to fuse SAR images with multispectral images, whose aim is to improve the interpretation ability of SAR images easily obtained at any time, rather than producing a fused image containing more information, which is the pursuit of previous fusion methods. Three different sensors, together with different working frequencies, polarization modes and spatial resolution SAR datasets, are used to evaluate the proposed method. Both visual evaluation and statistical analysis are performed, the results show that satisfactory fusion results are achieved through the proposed method and the interpretation ability of SAR images is effectively improved compared with the previous methods.
合成孔径雷达（SAR）是一种重要的遥感传感器，其应用越来越广泛。与传统光学传感器相比，它不易受外部环境干扰，具有很强的穿透性。受其工作原理限制，SAR图像不易解读，将SAR图像与光学多光谱图像融合是提高SAR图像可解读性的一个很好的解决方案。本文提出了一种基于非下采样剪切波变换和活跃度度量的新型图像融合方法，用于将SAR图像与多光谱图像融合，其目的是提高随时容易获取的SAR图像的解读能力，而不是生成一幅包含更多信息的融合图像，这是以往融合方法所追求的。使用三个不同的传感器，连同具有不同工作频率、极化模式和空间分辨率的SAR数据集，来评估所提出的方法。进行了视觉评估和统计分析，结果表明，通过所提出的方法获得了令人满意的融合结果，与以往方法相比，SAR图像的解读能力得到了有效提高。
