Uncertainty visualization of remote sensing crop maps enriched at parcel scale: a contribution for a more conscious GIS dataset usage
Uncertainty visualization of remote sensing crop maps enriched at parcel scale: a contribution for a more conscious GIS dataset usage
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地块尺度上丰富的遥感作物地图的不确定性可视化：对更自觉地使用 GIS 数据集的贡献
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
X. Pons
中科院分区：
文献类型：
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作者：
P. Serra;X. Pons
摘要不确定性是所有专题地图的一个固有问题，包括那些由遥感（RS）数据生成的专题地图。除其他外，用于获取地图的图像的特点或分类方法等因素可能造成不确定性程度的差异。鉴于地图的准确性是不空间均匀的，混淆矩阵并没有解决这个问题，本文提出了一种方法来可视化的空间不确定性的作物地图通过RS和丰富的地块尺度。最后的地图面积为3 323公顷，比例尺为1：35 000。 用于显示分类不确定性的估计量是“纯度”，即最终分配的类别所占的每个地块面积的百分比。该值是在地块尺度上分析的误分类概率的指标，这是比每像素方法在真实的管理中更有用的度量。
ABSTRACT Uncertainty is an inherent issue in all thematic maps, including those produced from remote sensing (RS) data. Factors such as the characteristics of the imagery used to obtain the map or the classification methods, among others, can contribute to differences in the level of uncertainty. Given that map accuracy is not spatially uniform and that confusion matrices do not resolve the issue, this paper proposes a methodology to visualize the spatial uncertainty of a crop map obtained through RS and enriched at parcel scale. The final map covers an area of 3323 ha represented at a scale of 1:35,000. The estimator used to show the classification uncertainty is ‘purity’, that is, the percentage of each parcel area occupied by the finally assigned category. This value is an indicator of misclassification probability analyzed at parcel scale, which is a more useful measure in real management than are per pixel approaches.