基于大比例尺航空影像共面约束条件的相机自检校方法

Camera Self-calibration Method Based on Large Scale Aerial Images Coplanarity Constraint

提出了基于大比例尺航空影像共面约束的相机自检校方法,该方法使用所有立体像对同名点基于共面约束对相机的内方位元素及畸变系数进行解算。首先进行航空影像同名点匹配,构建立体像对;然后基于共面约束使用直接解法和迭代优化进行相对定向,解算相机位置与姿态;最后使用最小二乘优化方法解算相机内方位元素和畸变系数。对于高分辨率大尺寸航空影像,图像中心及边缘的畸变差异较大,为了进一步提高解算精度,对图像进行网格区域划分解算畸变。使用大比例尺航空影像进行解算能真实精确反映航空摄影测量时所获取图像的相机参数和畸变系数,避免检校环境与使用环境不同解算得到的相机畸变参数不能真实反映所获取影像的畸变问题;使用所有同名点解算,避免由于选择不同特征点或控制点对检校精度的影响;通过区域网格划分,进一步提高了解算精度。对检校结果进行了分析,该方法精度较高,与基于室外检校场的精度相当,能真实精确反映航空摄影测量时所获取图像的相机参数和畸变系数,提高了三维重建的精度。

In this paper an automated camera self-calibration method based on large scale aerial images coplanadty constraint is proposed. The method uses stereo images corresponding points to compute camera inner parameters and the distortion coefficients based on coptanarity constraint. Firstly, aerial images corresponding points are matched and stereo images are constructed. Then the relative orientation of the camera is resolved by using the direct method and the iterative optimization based on the coplanarity constraint. Finally, the least square optimization method is used to calculate the inner parameters and the distortion coefficients of the camera. For large image the distortion of image boundary is more severe than center, in order to compute parameters more precisely mesh partition in arbitrary area of image is proposed. Compared with classical techniques which use expensive equipment or calibration field, the proposed technique can reflect the actual inner parameters of the camera in the real environment. Using all the corresponding points avoid the influence of choosing different feature points or control points on the calibration accuracy. Experimental results indicate that it is effective and flexible. The accuracy of the method is equivalent to that of calibration field, which can accurately reflect the camera parameters and the distortion coefficients of the image acquired by aerial photogrammetry.