Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data

Sa'ad Ibrahim , Heiko Balzter , Kevin Tansey
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Abstract

In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating multiple sensors for predicting aboveground biomass (AGB) is important for achieving high accuracy, reducing the dataset size by eliminating redundant and irrelevant spectral features is essential for enhancing the performance of machine learning algorithms. This accelerates the learning process, thereby developing simpler and more efficient models. Our results indicate that compared individual sensor datasets, the random forest (RF) classification approach using recursive feature elimination (RFE) increased the accuracy based on F score by 82.86 % and 26.19 respectively. The mutual information regression (MIR) method shows a slight increase in accuracy when considering individual sensor datasets, but its accuracy decreases when all features are taken into account for all models. Overall, the combination of features from the Landsat 8, ALOS PALSAR backscatter, and elevation data selected based on RFE provided the best AGB estimation for the RF and XGBoost models. In contrast to the k-nearest neighbors (KNN) and support vector machines (SVM), no significant improvement in AGB estimation was detected even when RFE and MIR were used. The effect of parameter optimization was found to be more significant for RF than for all the other methods. The AGB maps show patterns of AGB estimates consistent with those of the reference dataset. This study shows how prediction errors can be minimized based on feature selection using different ML classifiers.

利用 Landsat 8 和 ALOS PALSAR 数据预测非洲大草原地上生物量的机器学习特征重要性选择。
在遥感技术中,多个输入波段来自不同的传感器,覆盖电磁波谱的不同区域。每个光谱波段在土地利用/土地覆被特征描述中都发挥着独特的作用。例如,虽然整合多个传感器来预测地上生物量(AGB)对实现高精度非常重要,但通过消除冗余和不相关的光谱特征来减少数据集的大小,对提高机器学习算法的性能至关重要。这可以加速学习过程,从而开发出更简单、更高效的模型。我们的研究结果表明,与单个传感器数据集相比,使用递归特征消除(RFE)的随机森林(RF)分类方法提高了基于 F 分数的准确率,分别提高了 82.86 % 和 26.19 %。在考虑单个传感器数据集时,互信息回归(MIR)方法的准确率略有提高,但在所有模型都考虑所有特征时,其准确率则有所下降。总体而言,基于 RFE 选定的 Landsat 8、ALOS PALSAR 后向散射和高程数据的特征组合为 RF 和 XGBoost 模型提供了最佳的 AGB 估计。与 k-nearest neighbors(KNN)和支持向量机(SVM)相比,即使使用 RFE 和 MIR,也没有发现对 AGB 估计的显著改进。与所有其他方法相比,参数优化对 RF 的影响更为显著。AGB 地图显示的 AGB 估计模式与参考数据集的模式一致。这项研究显示了如何通过使用不同的多级分类器进行特征选择,将预测误差降到最低。
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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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