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首页> 外文会议>International symposium on spatial accuracy assessment in natural resources and environmental science >Tracking the positional uncertainty in 'ground truth'
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Tracking the positional uncertainty in 'ground truth'

机译：跟踪“地面真相”的位置不确定性

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When working with remotely-sensed data, the difficulties in accurately locating a single pixel on the ground are well documented. A number of phenomena contribute to this positional uncertainty, which is exacerbated by spatial bias within sensor footprints. The uncertainty propagated means that landscapes mapped for verification purposes can rarely be perfectly matched to the satellite images representing those landscapes. This paper evaluates four complementary methods in sequence, each devised to model or correct one type of positional uncertainty in verification datasets. A single test dataset is treated with all four methods in sequence, and subjected to fuzzy classification at each stage so that the effects of each method can be clearly quantified and compared. These methods include a Monte Carlo approach simulating random spatial errors, as well as several more systematic approaches.
机译：在使用远程感测的数据时，在地面上准确定位单个像素的困难是充分记录的。许多现象有助于这种位置不确定性，其在传感器占地面积内被空间偏压加剧。不确定性传播意味着映射用于验证目的的景观可以与代表那些景观的卫星图像很少匹配。本文评估了四种互补方法，每个互补方法，每个互补方法都设计为模型或校正验证数据集中的一种位置不确定性。单个测试数据集以序列的所有四种方法处理，并在每个阶段进行模糊分类，以便可以清楚地量化和比较各方法的效果。这些方法包括模拟随机空间误差的蒙特卡罗方法，以及几种更系统的方法。

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