二进小波增强与边缘局部信息FCM的SAR图像变化检测

Change Detection of SAR Images Using Dyadic Wavelet Enhancement and Edge Local Information FCM

合成孔径雷达图像中固有的相干斑噪声往往导致变化检测结果中存在大量虚警与漏警。针对这一问题,本文提出一种利用二进小波增强与边缘局部信息模糊C均值的变化检测方法。首先利用二进小波对对数比差异图进行自适应增强,平抑噪声的同时均衡灰度分布;然后,利用指数加权均值比算子对差异图进行边缘信息提取,修正局部信息模糊C均值算法中邻域窗内像素点权值,使邻域窗滑动至变化区域的边缘部分时能够对噪声切向平抑,保留细节信息。最后对差异图进行分割,得到变化检测结果二值图。仿真与实测数据实验结果表明,本文方法能够有效抑制相干斑噪声,同时对变化区域的细节保持效果较好。

Considering the speckle noise of the synthetic aperture radar （SAR） images which causes a large number of false-alarm and missing-alarm in the detection results, a novel method which can overcome speckle noise of SAR Images by using dyadic wavelet enhancement and local edge information fuzzy C-means （FCM） is proposed in this paper. First, log- ratio （LR） different images were enhanced adaptively by dyadic wavelet to denoise and redistribute the gray levels. Then, Edge information was detected by ratio of exponentially weighted average （ROEWA） operator to amend weight of the pixels in the local window. The step can selectively denoise to avoid interference of real edge when local window slide to edge re- gion, so it allows the algorithm to better preserve detail information. Finally, segmented the different images and obtained binary images. Results of simulation and real data processing demonstrate that the proposed method is effective to preserve details of change area and denoise.