A comprehensive evaluation of disturbance agent classification approaches: Strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction
A comprehensive evaluation of disturbance agent classification approaches: Strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction
复制标题
干扰源分类方法的综合评价：集成分类、多指标、时空变量和直接预测的优点
DOI：
10.1016/j.isprsjprs.2019.10.004
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发表时间：
2019
影响因子：
12.7
通讯作者：
Yoshida Shigejiro
中科院分区：
文献类型：
--
作者：
Shimizu Katsuto;Ota Tetsuji;Mizoue Nobuya;Yoshida Shigejiro
大地卫星时间序列图像用于探测森林扰动和对成因进行分类。各种研究对利用陆地卫星时间序列图像检测到的森林干扰因素进行了分类。然而，在不同的方法，最终分类的干扰剂的准确性很少进行评估。在这项研究中，我们研究了使用集合分类的有效性，以及多光谱和时空信息对两阶段预测中干扰因子分类的准确性（即，相对于检测到的干扰对干扰因素进行分类）和直接预测（即，干扰因子直接从陆地卫星时间信息分类）。预测变量来自于使用年度陆地卫星时间序列（2000-2018年）对五个光谱指数进行基于概率的时间分割的结果。我们比较了六种方法分类干扰剂。对于两阶段预测，我们研究了四种干扰检测方法：基于阈值的检测与一个单一的光谱指数，随机森林（RF）模型与一个单一的光谱指数，RF模型与多个光谱指数，RF模型与时空变量。检测到的干扰像素聚集到干扰补丁和分类到干扰剂。对于直接预测，两个RF模型一个只有时间变量，另一个与时空变量构建分类基于像素的干扰剂。使用时空变量直接预测的RF模型的总体准确率为92.4%，显著高于RF模型的两阶段预测（90.9%）。与基于阈值的检测相比，在干扰检测中仅基于单个光谱指数的RF模型的使用对于提高准确度是无效的;然而，在干扰检测中基于多个光谱指数的RF模型的使用提高了干扰剂的最终分类的准确度。在RF模型中引入空间变量可以有效地提高基于像素的直接预测的整体分类精度。然而，由于斑块中包含的空间信息，在两阶段预测中并不需要。虽然观察到的RF模型直接分类干扰剂的空间不连续的外观，这可能是一种替代方法，两个阶段的预测时，考虑到相对的分类性能和简单的实施。
Landsat time series images are used for the detection of forest disturbance and the classification of causal agents. Various studies have classified disturbance agents with respect to forest disturbance detected using Landsat time series images. However, the accuracy of the finally classified disturbance agents in different approaches is rarely evaluated. In this study, we investigated the effectiveness of using ensemble classification, and multiple spectral and spatio-temporal information for the accuracy of the classification of disturbance agents in two-stage prediction (i.e., disturbance agents are classified with respect to the detected disturbance) and direct prediction (i.e., disturbance agents are directly classified from Landsat temporal information). Predictor variables were derived from the results of the trajectory-based temporal segmentation of five spectral indices using an annual Landsat time series (2000–2018). We compared six approaches of classifying disturbance agents. For two-stage prediction, we investigated four disturbance detection approaches: threshold-based detection with a single spectral index, random forest (RF) model with a single spectral index, RF model with multiple spectral indices, and RF model with spatio-temporal variables. The detected disturbance pixels were aggregated to disturbance patches and classified into disturbance agents. For direct prediction, two RF models one with only temporal variables and the other with spatio-temporal variables were constructed to classify pixel-based disturbance agents. The overall accuracy of the RF model using spatio-temporal variables for direct prediction was 92.4% and significantly higher than that of the RF model for two-stage prediction (90.9%). The use of an RF model based only on a single spectral index in disturbance detection was not effective for improving accuracy compared with threshold-based detection; however, the use of an RF model based on multiple spectral indices in disturbance detection improved the accuracy of the final classification of disturbance agents. Introducing spatial variables in RF models was effective for improving the overall classification accuracy in pixel-based direct prediction. However, it was not necessary in two-stage prediction because of spatial information contained in the patches. Although a spatially discontinuous appearance was observed for the RF model for directly classifying disturbance agents, this could be an alternative approach to two-stage prediction when considering the relative classification performance and simplicity of implementation.