结合加速鲁棒特征的遥感影像半全局立体匹配

Semi-Global Stereo Matching of Remote Sensing Images Combined with Speeded up Robust Features

针对遥感影像半全局立体匹配(SGM)对噪声敏感及在视差不连续和弱纹理区域产生条纹导致匹配率低的问题,提出一种结合加速鲁棒特征(SURF)的遥感影像SGM算法。首先,用SURF计算遥感影像的特征匹配点和特征点主方向,并用快速最近邻搜索算法剔除错误的匹配点;然后,用Census变换计算两幅遥感影像的匹配代价,用特征点主方向来调整SGM算法在不同聚合路径方向上的路径权重;最后,用改进的加权联合双边滤波(WJBF)方法进行视差精化,以去除视差图中的噪声和条纹。在WorldView、IKONOS、高景一号遥感影像数据集上进行实验,结果表明,所提算法无论在主观视觉还是客观评价指标上都优于对比算法,有效解决了弱纹理和视差不连续区域中的条纹和噪声,提高了立体匹配精度。

The semi-global stereo matching(SGM) for remote sensing images is sensitive to noise and produces fringes in the areas with discontinuous disparity and weak texture, resulting in a low matching rate. An SGM algorithm for remote sensing images combined with speeded up robust features(SURF) is proposed herein. First, SURF is used to calculate feature-matching points and the main directions of the feature points in remote sensing images, and a fast nearest neighbor search algorithm is applied to eliminate the inaccurate matching points. Then, the Census transformation is used to calculate the matching cost of the two remote sensing images, and the path weight of the SGM algorithm in a different convergence path direction is adjusted by the main direction of the feature points. Finally, improved weighted joint bilateral filtering(WJBF) method is applied to refine the disparity to remove noise and fringes in the disparity maps. Experiments are performed on WorldView, IKONOS, and SuperView-1 remote sensing image datasets. Results show that the proposed algorithm is superior to the contrast algorithms in both subjective and objective evaluation indexes, effectively eliminating the fringes and noise in weak texture and disparity discontinuity area and improves the stereo-matching accuracy.