Abstract

Aiming at solving inaccurate and incomplete extraction of road in remote sensing images, this paper proposes an automatic extraction algorithm based on Rectangle Marked Point Process (RMPP). First, the RMPP is designed to model the road surface, which aims to obtain the centerline and width of road correctly. Secondly, under the framework of Bayesian theory, the proposed road extraction model is built by combining network reconstruction model and spectral measurement model. The former is to constrain the relationships between rectangles according to the structure characteristic of the road. The latter is to constrain the consistency between rectangle and road body in image. Also, to obtain optimal results, related transfer kernels of RJMCMC (Reversible Jump Markov Chain Monte Carlo) based simulation algorithm are designed accordingly. Finally, testing of the proposed method and comparing methods are carried out with different remote sensing datasets. Experimental results from the proposed algorithm show that the completeness, correctness and quality can reach 98%, 94% and 92%, respectively. Compared with the results from the comparing method qualitatively and quantitatively, it can be verified that the proposed method can not only extract the high-quality road networks from different datasets but also can obtain the width of the road simultaneously.

摘要

针对遥感图像中道路提取不准确和不完整的问题，提出了一种基于矩形标记点过程（RMPP）的自动提取算法。首先，RMPP旨在对道路表面进行建模，目的是正确获取道路的中心线和宽度。其次，在贝叶斯理论的框架下，结合网络重构模型和光谱测量模型，建立了道路提取模型。前者是根据道路的结构特征来约束矩形之间的关系。后者是为了限制矩形和图像中道路主体之间的一致性。此外，为了获得最佳结果，相应地设计了基于RJMCMC（可逆跳跃马尔可夫链蒙特卡罗）的仿真算法的相关传输内核。最后，该方法的测试和比较方法是在不同的遥感数据集上进行的。该算法的实验结果表明，该算法的完整性，正确性和质量分别达到98％，94％和92％。与定性和定量比较方法的结果进行比较，可以证明该方法不仅可以从不同的数据集中提取高质量的道路网络，而且可以同时获得道路的宽度。