The Effect of SAR Speckle Removal in SAR-Optical Image Fusion

Semih Gencay, Caner Özcan
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Abstract

Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.
SAR散斑去除在SAR-光学图像融合中的作用
由于合成孔径雷达(SAR)的成像机理和图像中的噪声,对场景中的目标进行视觉识别并不像在光学图像中那样容易。SAR图像的颜色信息有限,不能反映物体的光谱信息。另一方面,光学图像具有丰富的光谱信息。SAR-光学图像融合是一个重要的研究领域,它使任何人都可以方便地对SAR数据进行评估,但很难找到相同场景的匹配SAR和光学图像。为了克服这一困难,已经发表了Sentinel-1和Sentinel-2数据集,并使用各种方法进行了图像融合研究。然而,合并前去除SAR噪声对图像融合方法的影响尚未得到研究。在研究这种影响的研究中,使用了文献中使用的五种不同的融合算法,使用不同的降噪比对20种不同的图像组进行了测试。所获得的融合结果的成功与文献中广泛使用的五种不同指标进行了比较。实验得到的图像和度量结果表明,去除SAR数据中的散斑噪声对融合结果有积极的影响。
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