Coastal monitoring is an essential feature for the sustainable management of naturally vulnerable areas; however, data acquisition is a tedious task. We aim to identify an efficient method of automatic shoreline monitoring based on high water level detection using very high-resolution Pleiades images and taking as the pilot zone the Mostaganem coastline (Algeria). Through a comparative study between classification methods based on pixel- and object-based image analyses (PBIA and OBIA, respectively), algorithmic development and optimizing was conducted on two machine learning (ML) classifiers: random forest (RF), and support vector machine (SVM), and two segmentation algorithms: multiresolution (MRS) and meanshift (MSS). These classification methods yielded six different shorelines that were validated using an in-situ GPS survey shoreline acquired on the same day as the Pleiades image. The results showed that the OBIA generated a shoreline with a 5% to 25% better accuracy than that of PBIA using the same ML algorithm. Within the OBIA approach, MRS generated a shoreline with 20% higher accuracy compared to MSS, suggesting the importance of segmentation possessing. The RF based on MRS was the method that produced the shoreline at the best accuracy, where 55.5% of the extracted shoreline was within 1 pixel of the in situ shoreline. This method was successfully shown to be a good alternative for shoreline monitoring of sandy microtidal coasts, offering to coastal managers a reliable tool to complete the data and efficiently manage the coastal erosion.

中文翻译：

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使用光学卫星图像进行机器学习和海岸线监控：阿尔及利亚Mostaganem海岸线的案例研究

沿海监测是对自然脆弱地区进行可持续管理的基本特征；但是，数据采集是一项繁琐的任务。我们的目标是基于高分辨率的le宿星图像，并以Mostaganem海岸线（阿尔及利亚）为试验区，基于高水位检测，确定一种有效的自动海岸线监测方法。通过对基于像素和基于对象的图像分析的分类方法（分别为PBIA和OBIA）进行比较研究，对两个机器学习（ML）分类器进行了算法开发和优化：随机森林（RF）和支持向量机（SVM）和两种分割算法：多分辨率（MRS）和均值漂移（MSS）。这些分类方法产生了六种不同的海岸线，并通过与le宿星图像在同一天获取的原位GPS调查海岸线进行了验证。结果表明，使用相同的ML算法，OBIA生成的海岸线比PBIA生成的海岸线精度高5％至25％。在OBIA方法中，MRS生成的海岸线的精度比MSS高20％，这表明拥有分段的重要性。基于MRS的RF是以最佳精度产生海岸线的方法，其中提取的海岸线的55.5％位于原位海岸线的1个像素以内。该方法已成功证明是监测微潮沙质海岸海岸线的良好替代方法，为沿海管理人员提供了可靠的工具来完善数据并有效管理海岸侵蚀。