基于模糊连接度的高分辨率SAR图像道路自动提取

Automatic road extraction from high resolution SAR images based on fuzzy connectedness

针对高分辨率合成孔径雷达(SAR)图像受到乘性斑点噪声的影响,且道路环境复杂多变的问题,提出一种基于模糊连接度的高分辨率SAR图像道路自动提取方法。首先,对SAR图像进行斑点滤波,以降低斑点噪声的影响;其次,结合指数加权均值比(ROEWA)算子检测结果和模糊C均值(FCM)分割结果自动提取种子点,从而提高自动化程度;最后,利用以图像灰度和ROEWA检测算子边缘强度为特征的模糊连接度算法对种子点进行扩展提取道路,经形态学处理后得到最终结果。对两幅SAR图像进行实验,并与FCM方法分割出的道路结果进行比较,所提出的方法在提取完整率、正确率及检测质量上均优于模糊C均值方法。实验结果表明,所提出的方法能较有效地从高分辨率SAR图像中提取不同宽度和弯曲程度的道路,且无需人工输入种子点。

Focusing on the issue that high resolution Synthetic Aperture Radar （SAR） image is influenced by speckle noise and road environment is complex, an automatic road extraction method based on fuzzy connectedness was proposed. Firstly, a speckle filtering process was employed to SAR images to reduce the influence of speckle noise. Then seed points were extracted automatically by combining the results of Ratio of Exponentially Weighted Averages （ROEWA） detector and Fuzzy C-Means （FCM） clustering method. Finally, the roads were extracted by using fuzzy connectedness method which characterized by gray level and the edge intensity, and a morphology operation was done to optimize the final result. Comparison experiments between FCM based road extraction method and the proposed method were performed on two SAR images, the detection completeness, correctness and quality of the proposed method were better than those of FCM based road extraction method. The experimental results show that the proposed approach can effectively extract roads from high resolution SAR images without inputting seed points manually.