ABSTRACT

Wetlands are among the most important, yet in danger ecosystems and play a vital role for the well-being of humans as well as flora and fauna. Over the past few years, state-of-the-art deep learning (DL) tools have gained attention for wetland classification within the remote sensing community. However, the DL methods could have complex structure and their efficiency greatly depends on the availability of a large number of training data. Inspired by DL methods, yet with less complexity, the Deep Forest (DF) classifier is an advanced tree-based deep learning tool with a great capability for several remote sensing applications. Despite the effectiveness of DF classifiers, few research studies have investigated the potential of such a powerful technique for classification of remote sensing, with no documented research for wetland classification. Accordingly, the potential of the DF algorithm for the classification of wetland complexes has been investigated in this study. In particular, three well-known classifiers, namely Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Tree (ET), were used as the tree-based classifier to build DF, for which the hyper parameter tuning is carried out to ensure the optimum classification accuracy. Three well-known tree-based classification algorithms, namely Decision Tree (DT), Conventional Random Forest (CRF), and Conventional Extreme Gradient Boosting (CXGB), as well as a Convolutional Neural Network (CNN) are used as benchmark tools to compare the results obtained from the DF classifiers for wetland mapping. The results demonstrated that the DF-XGB classifier outperforms both DF-RF and DF-ET in terms of classification accuracy albeit with a longer training time. The results also confirmed the superiority of all three DF-based classifiers compared to the CRF and DT classifiers. For example, the DF-XGB improved the F1-score by 14%, 13%, 7%, 3%, and 1% for fen, swamp, marsh, bog, and shallow water, respectively, compared to the optimized CRF. The results indicated that the DF algorithm has great capability to be applied over large areas to support regional and national wetland mapping and monitoring.

摘要

湿地是最重要但处于危险之中的生态系统之一，对人类以及动植物群的福祉起着至关重要的作用。在过去几年中，最先进的深度学习 (DL) 工具在遥感界引起了湿地分类的关注。然而，DL 方法可能具有复杂的结构，其效率在很大程度上取决于大量训练数据的可用性。受 DL 方法的启发，但复杂度较低，深度森林 (DF) 分类器是一种先进的基于树的深度学习工具，具有适用于多种遥感应用的强大功能。尽管 DF 分类器很有效，但很少有研究调查过这种强大的遥感分类技术的潜力，也没有关于湿地分类的文献研究。因此，本研究研究了 DF 算法在湿地复合体分类方面的潜力。特别是使用三个著名的分类器，即极限梯度提升（XGB）、随机森林（RF）和额外树（ET）作为基于树的分类器来构建DF，并对其进行了超参数调整以确保最佳的分类精度。三种著名的基于树的分类算法，即决策树 (DT)、常规随机森林 (CRF) 和常规极限梯度提升 (CXGB)，以及卷积神经网络 (CNN) 作为基准工具进行比较从用于湿地测绘的 DF 分类器获得的结果。结果表明，DF-XGB 分类器在分类精度方面优于 DF-RF 和 DF-ET，尽管训练时间更长。结果还证实了所有三个基于 DF 的分类器与 CRF 和 DT 分类器相比的优越性。例如，与优化的 CRF 相比，DF-XGB 分别将 fen、沼泽、沼泽、沼泽和浅水的 F1 分数提高了 14%、13%、7%、3% 和 1%。结果表明，DF算法具有很强的大面积应用能力，支持区域和国家湿地测绘和监测。和浅水，分别与优化的 CRF 相比。结果表明，DF算法具有很强的大面积应用能力，支持区域和国家湿地测绘和监测。和浅水，分别与优化的 CRF 相比。结果表明，DF算法具有很强的大面积应用能力，支持区域和国家湿地测绘和监测。