结合张量投票和Snakes模型的SAR图像道路提取

目的 Snakes模型对曲线轮廓具有良好的拟合能力,被广泛应用于遥感图像的道路提取。但SAR图像受乘性斑点噪声影响严重,因此利用Snakes模型从SAR图像提取道路时,传统的以图像灰度负梯度为外部能量的方法难以取得理想结果。针对这一问题,利用计算机视觉中的张量投票算法可以从噪声掩盖的图像中提取显著结构特征的特点,将张量投票与Snakes模型结合从SAR图像提取道路。方法 首先利用模糊C均值分割法从SAR图像中分割出道路类,然后对道路类进行张量投票获得每点的曲线显著性值,最后以该曲线显著性值的负值作为Snakes模型外部能量从SAR图像提取道路。在Snakes模型能量最小化阶段,提出了一种优化的拟合策略,一边内插节点一边最小化Snakes模型能量。结果 利用机载和星载不同场景的SAR图像进行实验,与同类的基于Snakes模型的半自动方法相比,本文方法对曲率较大的道路仅需较少控制点即可取得较好的拟合效果;与基于MRF模型的自动方法相比,本文方法对道路提取的完整率、正确率、检测质量都优于基于MRF模型的方法,并且提取的时间远远快于基于MRF模型的方法,对于大范围的道路网提取将更为实用。结论 本文方法充分考虑到道路的几何形态特征,利用张量投票算法对该特征进行量化,并利用优化的拟合策略来最小化Snakes模型能量来提取道路。基于机载和星载SAR图像的实验表明本文方法可以较好地提取不同场景中的主要道路目标和道路网。

Road extraction from SAR images using tensor voting and Snakes model

Objective Snakes models can effectively fit curve features and are thus widely used to extract roads from remote sensing images. However, when used to extract roads from synthetic aperture radar (SAR) images, traditional Snakes models that utilize the negative gradient of images as external energy cannot obtain the desired results because of serious speckle noise. To address this issue, we employed a tensor voting method in improving Snakes models because such method can extract salient structures from images influenced by noises. Method Road class was first segmented from SAR images using the FCM clustering method. Then, the saliency value of the curve features was obtained by employing the tensor voting method on the extracted road class. Finally, the negative normalized saliency value of the curve features was used as the external energy of the snakes model to extract roads. To minimize the energy of the snakes model, a strategy for minimizing energy while interpolating nodes was proposed. Result Road extraction experiments were performed on different scenes of spaceborne and airborne SAR images. Compared with a similar method based on the snakes model, the proposed modelachieved better fitting results with less control points. Moreover, the proposed method showed better detection completeness, correctness, and quality than the MRF-based method. The proposed method also demonstrated a shorter detection time, which is a practical feature for wide-range road network extraction. Conclusion The proposed method quantified the geometric characteristics of roads through tensor voting. An optimized fitting strategy was used to minimize the energy consumption of the snakes model for road extraction. The experiments on spaceborne and airborne SAR images proved that main roads in rural and urban scenes can be effectively extracted using the proposed method.