顾及纹理特征的航空影像自适应密集匹配方法

An Adaptive Dense Matching Method for Airborne Images Using Texture Information

半全局匹配实质是在视差连续性假设下的离散优化方法。为克服视差断裂带影响,依赖一组参数控制视差不一致性。若参数过小,在平面内难以保证视差连续性,产生明显噪声,导致凹凸不平现象;若参数过大,将致使物体表面过于平滑,难以保留视差断裂等重要特征。为克服上述问题,本文提出了一种顾及纹理特征的自适应密集匹配方法:首先,检测影像纹理特征并定量表达纹理丰富性程度;其次,依据纹理丰富程度与视差差异存在正相关的规则知识,实现匹配参数依据纹理信息的自适应选择;最后,采用上述参数进行自适应的半全局匹配。通过ISPRS基准数据集和国产SWDC-5获取的倾斜影像进行试验分析证明,本文方法能够有效减少低纹理区域匹配噪声,同时更有效保留边缘特征。

Semi-global matching （SGM） is essentially a discrete optimization for the disparity value of each pixel, under the assumption of disparity continuities. SGM overcomes the influence of the disparity discontinuities by a set of parameters. Using smaller parameters, the continuity constraint is weakened, which will cause significant noises in planar and textureless areas, reflected as the fluctuations on the final surface reconstruction. On the other hands, larger parameters will impose too much constraints on continuities, which may lead to losses of sharp features. To address this problem, this paper proposes an adaptive dense stereo matching methods for airborne images using with texture information. Firstly, the texture is quantified, and under the assumption that disparity variation is directly proportional to the texture information, the adaptive parameters are gauged accordingly. Second, SGM is adopted to optimize the discrete disparities using the adaptively tuned parameters. Experimental evaluations using the ISPRS benchmark dataset and images obtained by the SWDC-5 have revealed that the proposed method will significantly improve the visual quatities of the point clouds.