耦合非局部自相似性与散度的SAR与光学影像融合

An SAR and optical image fusion algorithm coupling non-local self-similarity and divergence

SAR与光学影像的高质量融合应用是目前研究的热点问题之一,然而二者间辐射差异大、灰度相关性弱等问题,严重影响了融合质量。为此,该文提出一种耦合非局部自相似性与散度的SAR与光学影像融合算法。首先在频率域将影像分解,然后使用非局部方向熵和散度作为特征量分别指导低频和高频分量进行融合,最后将融合分量重建,得到兼具清晰结构特征和丰富光谱信息的融合影像。通过对比实验,证明所提算法在融合SAR与光学影像方面的有效性,及其在保持结构特征和减小光谱扭曲方面的优越性。

Currently,the high-quality fusion of SAR and optical images is a hot research topic.However,the significant radiation difference and weak gray correlation between SAR and optical images greatly reduce the fusion quality.In this regard,this study proposed a SAR and optical remote sensing image fusion algorithm that coupled non-local self-similarity and divergence.First,images were decomposed in the frequency domain.Then,the non-local directional entropy and divergence were used as characteristic parameters to guide the fusion of low-and high-frequency components,respectively.Finally,the fusion components were reconstructed to obtain fusion images with clear structural features and rich spectral information.The comparative experiments verified the effectiveness of the proposed algorithm in fusing SAR with optical images and its superiority in maintaining structural features and reducing spectral distortion.