基于双阈值SSDA模板匹配的遥感图像道路边缘检测研究

Road edge detection based on dual-threshold SSDA template matching

利用遥感图像提取道路边缘信息可以简化常规城市道路的测绘工作。根据一般道路在图像上的影像特征对图像进行模型化处理,提出了基于双阈值的序贯相似性检测算法(sequential similarity detection algorithm,SSDA)的模板匹配算法,并在该算法的基础上,提出了一个基于减小增长误差的算子,用以减少由于样本数过多而产生的增长误差。与其他算法相比,该算法能够更有效地获取道路边缘信息,在准确提取路面边缘信息的同时,能对预处理中难以完全提取的路面部分进行修复,减少处理过程,从而提高处理效率。

Extracting road edge information from remote sensing image can simplify the conventional urban road mapping work. Based on the general road image features, this paper proposes a dual-threshold SSDA （ sequential similarity detection algorithm） template matching method in an image processing model. And on the basis of the general SSDA, another algorithm is presented to reduce the excessive number of samples responsible for the error growth. Compared with other algorithms, this algorithm can more effectively access the road edge information extraction. As for some parts of the road which cannot be completely extracted through pretreatment process, the detection results can be corrected to reduce treatment, and hence the processing efficiency will be improved.