摘要
SAR和可见光图像成像机理不同,图像差异较大,较难取得良好的融合效果。本文面向目标识别,通过分析图像的成像机理,首先在NSCT融合框架下,将SAR图像中重要的目标信息加入到可见光图像中,并尽可能多的保留源图像的边缘细节信息;再结合数学形态学和多尺度空间理论,提取源图像的亮、暗细节特征,进行特征级融合,得到亮、暗细节特征显著增强的融合图像。实验结果表明,本文算法有效的融合了SAR图像的目标信息,并增强了源图像的细节特征,达到了较好的视觉效果,提高了图像的目标检测和识别能力。
Different imaging mechanism of Synthetic Aperture Radar （SAR） and visible images in the general fusion methods make it difficult to achieve good fusion effect. For the target recognition, firstly get fusion image by Nonsubsampled Contourlet Transform （NSCT） to fuse the important objective information of SAR image into visible image, and retain detail information as much as possible. Then, combine mathematical morphology with multiscale space theory to get the bright and dark detail features of the original images. Finally, obtain fused image with significantly enhanced bright and dark detail features. Experimental result shows that the proposed algorithm can effectively fuse target information of SAR image, enhance the detail features of the source image, achieve a better visual effect, and improve the capability of target detection and identification.
出处
《光电工程》
CAS
CSCD
北大核心
2014年第3期55-60,共6页
Opto-Electronic Engineering
基金
山西省回国留学人员科研资助项目(20120706ZX)
教育部高等学校博士学科点专项科研基金博导类资助课题(20121420110004)
