摘要
给出了一种检测高分辨率SAR图像道路目标的方法。该方法首先对原始图像进行一系列的预处理,然后利用Hough变换识别道路。在提取感兴趣区域时,该算法采用了高斯概率迭代方法代替了一般的CFAR检测或阈值分割;在进行Hough变换时,该算法用平均Hough变换代替一般的Hough变换;在检测变换域峰值点时,采用了结合全局CFAR检测的最大值点搜索的方法;最后对检测出来的准道路,该算法根据道路的独具性质进行鉴别,以确定其是否确实为道路。使用MSTARRedstone实测杂波图进行实验,取得了满意的效果。
Extraction of roads from the high resolution SAR imagery is described. The method employs Hough Transform to identify roads, followed by a series of pre-processes. Gaussian probability iteration method instead of usually used CFAR detector or threshold segmentation is employed to identify the regions of interest. The generally used Hough Transform is replaced by the average Hough Transform. The peak values in the transform area are detected by a maximum search integrating global CFAR detector. Finally, a unique geometric constraint is used to discriminate the potential roads. High-resolution SAR images of MSTAR Redstone are used to illustrate our method, and the performance is satisfactory.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2004年第1期50-55,共6页
Journal of National University of Defense Technology
基金
国家部委资助项目(41322020401)
