Geographically weighted accuracy for hard and soft land cover classifications: 5 approaches with coded illustrations
Geographically weighted accuracy for hard and soft land cover classifications: 5 approaches with coded illustrations
复制标题
硬土地覆盖和软土地覆盖分类的地理加权准确性：带有编码插图的 5 种方法
DOI：
10.1080/01431161.2023.2264503
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发表时间：
2023
影响因子：
3.4
通讯作者：
Comber A
中科院分区：
文献类型：
--
作者：
Comber A
本文探讨了不同的地理加权（GW）的方法来计算空间分布的准确性/不确定性的措施，巩固现有的方法，并提出了2个新的。GW框架使用移动窗口或内核来提取和加权数据子集，从中计算本地（即空间分布）统计数据或度量。一个验证数据集与硬和软分类是用来说明的方法。它包含观测到的实地调查数据（通常也来自更高分辨率的图像），以及来自模糊c均值分类的预测数据。硬类被用来估计空间分布的整体措施，用户和生产者的准确性在两种方式。首先，通过将它们概念化为从广义线性回归模型（GLM）估计的概率，扩展到地理加权GLM。第二，通过构建本地GW对应矩阵，然后从这些计算本地精度测量。软类被用来计算每个类的措施，从预测和观察到的模糊成员之间的绝对差异的模糊确定性。然后，提出了一种新的模糊确定性逻辑，并用于创建模糊混淆矩阵和每类措施的模糊遗漏和委员会的错误，支持措施的模糊用户和生产者的信任。这些被扩展到GW的情况下，产生空间分布的措施。最后，软分类概念化的组成数据和差异的措施估计使用艾奇逊距离。在每一种情况下，当地的硬和软的准确性和确定性的措施是插在一个1公里的网格，以估计精度表面。本次审查的背景是越来越多的训练和验证数据的操作使用，通常具有大量的记录，包含硬类和软类。本文提供了用于进行所有分析的数据和R代码，支持对此类数据进行更细致的分析。
This paper examines different geographically weighted (GW) approaches for calculating spatially distributed measures of accuracy / uncertainty, consolidating current approaches and proposing 2 new ones. GW frameworks use a moving window or kernel to extract and weight data subsets, from which local (ie spatially distributed) statistics or metrics are calculated. A validation dataset with hard and soft classifications is used to illustrate the approaches. It contains observed field survey data (also commonly derived from higher resolution imagery), and predicted data from a fuzzy c-means classification. The hard classes were used to estimate spatially distributed measures of overall, user's and producer's accuracies in two ways. First, by conceptualising them as probabilities to be estimated from generalised linear regression models (GLMs), extended into Geographically Weighted GLMs. Second, by constructing local GW correspondence matrices and then calculating local accuracy measures from these. The soft classes were used to calculate per-class measures of fuzzy certainty from the absolute difference between predicted and observed fuzzy memberships. Then, a novel fuzzy certainty logic is proposed and used to create fuzzy confusion matrices and per-class measures of fuzzy omission and commission error, supporting measures of fuzzy user's and producer's certainties. These were extended to the GW case to generate spatially distributed measures. Finally, the soft classifications were conceptualised as compositional data and measures of difference were estimated using Aitchison distances. In each case, the local hard and soft accuracy and certainty measures were interpolated over a 1 km grid to estimate accuracy surfaces. The context for this review is the increasing operational use of training and validation data, often with high numbers of records, containing both hard and soft classes. The data and R code used to undertake all the analyses in this paper are provided, supporting more nuanced analyses of such data.
DOI：
10.1109/igarss39084.2020.9323939
发表时间：
2020
期刊：
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
影响因子：
--
作者：
Tsutsumida Narumasa;Yoshida Takahiro;Murakami Daisuke;Nakaya Tomoki
通讯作者：
Nakaya Tomoki
影响因子：
2.9
作者：
A. Comber;P. Harris
通讯作者：
A. Comber;P. Harris
影响因子：
13.5
作者：
Foody, Giles M.
通讯作者：
Foody, Giles M.