该文结合基于非下采样方向滤波-双树复小波变换(NonSubsampled Direction Filter Bank-Dual-TreeComplex Wavelet Transform,NSDFB-DTCWT)的局部混合滤波算法和Dempster-Shafet(DS)证据理论提出一种基于局部混合滤波的SAR图像边缘检测...该文结合基于非下采样方向滤波-双树复小波变换(NonSubsampled Direction Filter Bank-Dual-TreeComplex Wavelet Transform,NSDFB-DTCWT)的局部混合滤波算法和Dempster-Shafet(DS)证据理论提出一种基于局部混合滤波的SAR图像边缘检测算法。该算法首先对SAR图像进行局部混合滤波,然后对不同尺度滤波图像使用指数加权均值比(Ratio Of Exponentially Weighted Averages,ROEWA)算子检测边缘的强度,再使用Canny算子检测边缘的方向,从而得到SAR图像各尺度上的边缘,最后使用DS证据理论融合各尺度的边缘形成原始SAR图像的边缘。实验结果表明:该文所提出的算法具有很好的边缘检测效果,检测到的SAR图像的边缘定位准确和完整,且伪边缘较少。展开更多
A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly foc...A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.展开更多
文摘该文结合基于非下采样方向滤波-双树复小波变换(NonSubsampled Direction Filter Bank-Dual-TreeComplex Wavelet Transform,NSDFB-DTCWT)的局部混合滤波算法和Dempster-Shafet(DS)证据理论提出一种基于局部混合滤波的SAR图像边缘检测算法。该算法首先对SAR图像进行局部混合滤波,然后对不同尺度滤波图像使用指数加权均值比(Ratio Of Exponentially Weighted Averages,ROEWA)算子检测边缘的强度,再使用Canny算子检测边缘的方向,从而得到SAR图像各尺度上的边缘,最后使用DS证据理论融合各尺度的边缘形成原始SAR图像的边缘。实验结果表明:该文所提出的算法具有很好的边缘检测效果,检测到的SAR图像的边缘定位准确和完整,且伪边缘较少。
基金supported by National Natural Science Foundationof China (No. 60802061)Natural Science Research Item of the Education Department of Henan Province (No. 2008B510001)Innovation Scientists and Technicians Troop Construction Projects of Henan Province (No. 084100510012)
文摘A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
