Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.

中文翻译：

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融合 Sentinel-1 SAR 和 Sentinel-2 MSI 数据，以实现埃塞俄比亚贡德尔市准确的城市土地利用-土地覆盖分类

有效的城市规划和管理依赖于准确的土地覆盖测绘，这可以通过遥感数据和机器学习算法的结合来实现。本研究旨在探索和展示整合 Sentinel-1 SAR 和 Sentinel-2 MSI 卫星图像对埃塞俄比亚贡德尔市城市土地覆盖分类的潜在好处。这项研究工作使用了 2023 年 Sentinel-1A 的合成孔径雷达 (SAR) 数据和 Sentinel-2B 的多光谱仪器 (MSI) 数据。支持向量机（SVM）和随机森林（RF）机器学习算法用于分类过程。Google Earth Engine (GEE) 用于遥感数据的处理、分类和验证。研究结果为使用不同数据集（即 Sentinel 2B 多光谱仪器 (MSI) 和 Sentinel 1A 合成孔径雷达 (SAR)）进行图像分类的支持向量机 (SVM) 和随机森林 (RF) 算法的性能评估提供了宝贵的见解。 ） 数据。当应用于 Sentinel 2B MSI 数据集时，SVM 和 RF 的总体准确度 (OA) 均为 0.69，Kappa 得分为 0.357，表明具有中等程度的一致性。对于 Sentinel 1A SAR 数据，SVM 保持相同的 OA 0.69，但显示出改进的 Kappa 分数 0.67，表明其适合 SAR 图像分类。相比之下，RF 使用 Sentinel 1A SAR 数据实现了略低的 OA，为 0.66。然而，当将 Sentinel 2B MSI 和 Sentinel 1A SAR 的数据集合并时，SVM 实现了令人印象深刻的 OA 0.91，Kappa 得分为 0.80，而 RF 实现了 0.81 的 OA，Kappa 得分为 0.809。这些发现凸显了融合多个来源的卫星数据以提高图像分类算法的准确性和有效性的潜力，使其成为各种应用的宝贵工具，包括土地利用测绘和环境监测。