半全局约束下的多基线立体影像MVLL匹配方法

MVLL Match Method for Multi-baseline Stereo Imagery Based on Semi-global Constraint

  • 摘要: 多视铅垂线轨迹法(multi-view vertical line locus,MVLL)能够以物方地面铅垂线为几何约束,匹配获取物方空间点的最佳高程,是一种实用的多基线立体影像匹配方法。针对MVLL匹配方法中的物方空间点独立匹配、缺少整体性约束问题,将物方空间局部光滑特性应用至匹配过程,提出了一种半全局约束下的多基线立体影像MVLL匹配方法。首先,将物方空间点沿地面铅垂线上的准确高程搜索,等效至像方空间沿核线方向上的准确视差搜索;其次,采用MVLL匹配方法计算物方空间点在多张影像上的等效像方匹配测度;然后,采用半全局匹配法(semi⁃global matching, SGM)对匹配测度进行多路径聚合分析,得到物方空间局部光滑约束下的等效视差图;最后,将等效视差图转化为物方空间点匹配高程,并对多基线立体影像进行多分辨率匹配处理,实现整体最优条件下的MVLL匹配。采用多类型地形特征与局部区域影像进行匹配实验与对比分析,实验结果表明,所提方法能够对多类型地形特征的物方空间匹配测度进行优化,获取更加可靠的匹配结果,具有更高的影像匹配性能。

     

    Abstract:
      Objectives  Multi-view vertical line locus (MVLL) is a practical multi-baseline stereo image matching method, which can match and obtain the best elevation of ground points with the ground plumb line as geometric constraint. Aiming at solving the problem of independent ground point matching and lacking integrity constraint in MVLL matching method, the local smooth property of object space is applied to the matching process, and the MVLL matching method for multi-baseline stereo imagery based on semi-global constraint is proposed.
      Methods  Firstly, the accurate elevation of ground points is searched along the ground plumb line, which can be equivalent to the accurate parallax search along the epipolar image space. Secondly, the MVLL matching method is used to calculate the equivalent image matching measure of ground points on multiple images. Then, the semi⁃global matching (SGM) method is used to aggregate and analyze the matching measure through multi-path, and the equivalent disparity map under the local smooth constraint of the object space is obtained. Finally, the equivalent parallax map is converted to matching elevation of the ground points. And through multi-resolution matching of the multi-baseline stereo imagery, MVLL matching is realized under the overall optimal conditions and greatly integrated with SGM matching method.
      Results  The effectiveness of the proposed method is verified by experiments and analysis of various terrain features, including uneven surface feature, similar texture feature and occlusive feature. Comparative experiments are also conducted on local image areas.
      Conclusions  The experimental results show that the proposed method can optimize the object space matching measure of different terrain features, obtain more reliable matching results, and have higher image matching performance.

     

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