Abstract

Synthetic aperture radar (SAR) data has been an alternative for monitoring ground targets, especially in areas with cloud cover. This study evaluates the potential of Sentinel-1A attributes for mapping land use and land cover (LULC) in a region of the Brazilian Amazon, using two different machine learning classifiers: Random Forest (RF) and Support Vector Machine (SVM). Different scenarios were used that combined backscattering, polarimetry, and interferometry to the classification process, which was divided into two phases to improve the results. The RF shows superiority over the SVM for almost all scenarios for the two phases of the mapping. The scenario with all data, presented the best results with both classifiers. The final maps with RF and SVM, obtained a global accuracy of 82.7% and 74.5%, respectively. This study demonstrated the potential of Sentinel-1 to map LULC classes in the Amazon region using a classification in two phases.

摘要

合成孔径雷达 (SAR) 数据已成为监测地面目标的替代方法，尤其是在有云层的区域。本研究使用两种不同的机器学习分类器：随机森林 (RF) 和支持向量机 (SVM)，评估 Sentinel-1A 属性在巴西亚马逊地区绘制土地利用和土地覆盖 (LULC) 的潜力。使用了不同的场景，将反向散射、偏振测量和干涉测量结合到分类过程中，分类过程分为两个阶段以改进结果。对于映射的两个阶段，RF 在几乎所有场景中都显示出优于 SVM 的优势。包含所有数据的场景在两个分类器中都呈现出最好的结果。RF 和 SVM 的最终映射分别获得了 82.7% 和 74.5% 的全局精度。