基于多模态特征的光鄄SAR 图像融合配准算法

针对可见光和合成孔径雷达(SAR: Synthetic Aperture Radar)图像融合问题, 在图像预处理基础上, 从像素级特征、纹理级特征及边缘轮廓特征等多模态入手, 优化现有同源图像的配准融合算法。利用改进的SURF(Speeded Up Robust Features)算子、纹理分析及轮廓提取算法, 获取待融合图像的多模态和多尺度特征。通过模糊尺度标准化, 使异源图像特征对能更好地适应特征间的差异性, 从而能进行相似性的比较, 结合模糊相关系数法, 确保配准融合的精度, 实现光鄄SAR 图像信息的有效融合。与传统配准融合方法进行比较的实验结果表明, 该算法可提高光鄄SAR 配准的精度和适应性, 使配准融合的平均准确率达到87. 7%, 可满足较高精度的配准融合需求。

Optical-SAR Image Registration Using Multimodal Features Fusion Algorithm

According to the image fusion of optical and SAR(Synthetic Aperture Radar), the multimodal and multiscale features including pixel features, texture features and edge features were analyzed in order to improve the traditional homologous image registration and fusion algorithm. Then the improved SURF(Speeded Up Robust Features) operator, texture analysis and contour extraction algorithm were adopted to obtain the multimodal and multiscale features of the heterologous images. By standardization algorithm of the fuzzy scale and dimension, the differences between the feature pairs of the heterologous images were overcome, which made the matching of the feature pairs available. The accuracy of registration and fusion were ensured through the method of fuzzy
correlation coefficient, and the registration and fusion of optical-SAR images were completed. Finally, the modified algorithm was verified and compared with the traditional fusion methods. Experimental results show that the multimodal registration and fusion algorithm can improve the precision and adaptability of optical-SAR registration. The average accuracy rate of registration and fusion can reach to 87. 7%, which can satisfy the requirement of high precision registration and fusion.