面向数字地形模型生成的高精度图像匹配

Armin W. Gruen, Emmanuel P. Baltsavias
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引用次数: 75

摘要

将灰度相关与几何条件相结合的自适应最小二乘相关(ALSC)应用于网格采样模式,用于从航空立体模型生成数字地形模型(DTM)。利用该方法确定了大规模数字化图像中不同几何和对比度质量目标的高度,并与人工测量结果进行了比较。根据高度近似,两种解决方案的平均差异为0.016 ~ 0.038%,最大差异为0.037 ~ 0.120%。调查的大多数病例需要在1-2%汞柱水平的初始高度近似值。在每个迭代步骤中,平均高度误差为0.2% hg。所施加的约束被证明定义了灰度图像块的运动,从而限制了搜索区域和假相关的概率,提高了高度确定的精度和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-precision image matching for digital terrain model generation

The Adaptive Least Squares Correlation (ALSC), combining gray-level correlation with geometric conditions, was applied to a grid-sampling mode for generation of Digital Terrain Models (DTM) from aerial stereo models.

The heights of targets of different geometric and contrast quality in large-scale digitized images were determined using this technique and compared to results from manual measurements. Both solutions showed an average difference of 0.016–0.038% and a maximum differences of 0.037–0.120% flying height (hg), depending on the height approximation. Most cases investigated required an initial height approximation at the 1–2% hg level. An average height error of 0.2% hg was cleared in each iteration step. The constraints imposed proved to define the movement of the gray-level image patches, thus limiting the search area and probability of false correlation, and increasing the precision and reliability of height determination.

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