Measuring Uncertainty in Class Assignment for Natural Resource Maps under Fuzzy Logic
Measuring Uncertainty in Class Assignment for Natural Resource Maps under Fuzzy Logic
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DOI：
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发表时间：
1997
影响因子：
1.3
通讯作者：
A. Zhu
中科院分区：
文献类型：
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作者：
A. Zhu
在分类过程中，与将地理实体分配给类相关的不确定性有两种。第一个与实体在规定的类集合中的模糊归属有关，第二个与实体与被分配到的类的原型的偏差有关。本文认为，如果在空间数据表示中采用相似模型，这两种不确定性是可以估计的。在该相似度模型下，模糊隶属度的不确定性可以用隶属度分布的熵度量或隶属度残差的度量来近似。与原型定义的偏差相关的不确定性可以使用成员夸大度量来估计。一项使用土壤地图的案例研究表明，高熵值出现在土壤似乎处于过渡状态的地区，而错误分类的地区具有较高的熵值。对于土壤专家在识别土壤类型和预测其空间分布方面信心较低的地区，隶属度夸大程度很高。这些措施有助于查明，高海拔地区的地图绘制精度很高，在绘制低海拔地区的土壤资源图时需要减少误差。
There are two kinds of uncertainty associated with assigning a geographic entity to a class in the classification process. The first is related to the fuzzy belonging of the entity to the prescribed set of classes and the second is associated with the deviation of the entity from the prototype of the class to which the entity is assigned. This paper argues that these two kinds of uncertainty can be estimated if a similarity model is employed in spatial data representation. Under this similarity model, the uncertainty of fuzzy belonging can be approximated by an entropy measure of membership distribution or by a measure of membership residual. The uncertainty associated with the deviation from the prototype definitions can be estimated using a membership exaggeration measure. A case study using a soil map shows that high entropy values occur in areas where soils seem to be transitional and that areas which are mis-classified have higher entropy values. The membership exaggeration is high for areas where soil experts have low confidence in identifi-ing soil types and predicting their spatial distribution. These measures helped in identifying that the high elevation areas were mapped with high accuracy and that error reduction efforts are needed in mapping the soil resource in the low elevation areas.