ABSTRACT Globally, Smallholder farming systems (SFS) are recognized as one of the most important pillars of rural economic development and poverty alleviation because of their contribution to food security. However, support for this agricultural sector is hampered by lack of reliable information on the distributions and acreage of smallholder fields. This information is essential in not only monitoring food security and informing markets but also in guiding the determination of levels of support required from government by individual farmers. There is urgent need for robust techniques that can be used to cost-effectively and time-efficiently map smallholder crop fields especially in Sub-Saharan Africa and Asia. This study attempts to do this by using an approach in which optical and Synthetic Aperture Radar (SAR) data are systematically combined and classified using Extreme Gradient Boosting (Xgboost). We also investigated model stacking as another technique to improve classification accuracy. We combined Xgboost with Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB). The combined use of multi-temporal Sentinel-2 bands, spectral indices, and Sentinel-1 produced better results than exclusive use of optical data (α = 0.95, p = 0.0005). Furthermore, stacking of classification algorithms based on model comparisons achieved higher accuracy than stacking the algorithms indiscriminately (α = 0.95, p = 0.0100). Through systematic fusion of SAR and optical data and hyper-parameter tuning of Xgboost, we achieved a maximum classification accuracy of 97.71%, while achieving a maximum accuracy of 96.06% through model stacking. This highlights the importance of multi-sensor data fusion and multi-classifier systems when mapping fragmented agricultural landscapes.

中文翻译：

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通过图像融合和模型堆叠增强小农作物种植景观的映射

摘要 在全球范围内，小农耕作系统 (SFS) 因其对粮食安全的贡献而被公认为农村经济发展和扶贫的最重要支柱之一。然而，由于缺乏关于小农田的分布和面积的可靠信息，对该农业部门的支持受到阻碍。这些信息不仅对于监测粮食安全和为市场提供信息至关重要，而且对于指导确定个体农民所需的政府支持水平也是必不可少的。迫切需要可用于经济高效且时间高效地绘制小农农田地图的强大技术，尤其是在撒哈拉以南非洲和亚洲。本研究试图通过使用一种方法来实现这一点，在该方法中，光学和合成孔径雷达 (SAR) 数据使用极限梯度增强 (Xgboost) 系统地组合和分类。我们还研究了模型堆叠作为另一种提高分类精度的技术。我们将 Xgboost 与随机森林 (RF)、支持向量机 (SVM)、人工神经网络 (ANN) 和朴素贝叶斯 (NB) 相结合。多时间 Sentinel-2 波段、光谱指数和 Sentinel-1 的组合使用比单独使用光学数据产生更好的结果 (α = 0.95, p = 0.0005)。此外，基于模型比较的分类算法堆叠比不加区别地堆叠算法实现了更高的准确性（α = 0.95，p = 0.0100）。通过SAR和光学数据的系统融合以及Xgboost的超参数调优，我们实现了最高 97.71% 的分类准确率，同时通过模型堆叠实现了最高 96.06% 的准确率。这突出了多传感器数据融合和多分类器系统在绘制支离破碎的农业景观时的重要性。