Abstract The eastern part of the Rich area consists of the massive Paleozoic and Meso-Cenozoic cover formations that present the geodynamic development of the study area, where is characterized by various carbonate facies of Jurassic age. The geographical characteristic of the study area leaves the zone difficult to map by conventional methods. The objective of this work focuses on the mapping of the constituent lithological units of the study area using multispectral data of Landsat OLI, ASTER, and Sentinel 2A MSI. The processing of these data is based on a precise methodology that distinguishs and highlights the limits of the different lithological units that have an approximate similarity of spectral signature. Three techniques were used to enhance the image including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). Lithological mapping was performed using two types of supervised classification : Maximum likelihood classifier (MLC) and Support Vector Machine (SVM). The results of processing data show the effectiveness of Sentinel 2A data in mapping of lithological units than the ASTER and Landsat OLI data. The classification evaluation of two methods of the Sentinel 2A MSI image showed that the SVM method give a better classification with an overall accuracy of 93,93% and a Kappa coefficient of 0.93, while the MLC method present an overall accuracy of 82,86% and a Kappa coefficient of 0.80. The results of mapping obtained show a good correlation with the geological map of the study area as well as the efficiency of remote sensing in identification of different lithological units in the Central High Atlas.

中文翻译：

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Landsat OLI、ASTER 和 Sentinel 2A 数据在岩性绘图中的比较：丰富地区的案例研究（Central High Atlas，摩洛哥）

摘要 富集区东部由大量古生代和中新生代盖层组成，代表了研究区的地球动力学发育，以侏罗纪时期各种碳酸盐岩相为特征。研究区的地理特征使得该区域难以通过常规方法绘制。这项工作的目标侧重于使用 Landsat OLI、ASTER 和 Sentinel 2A MSI 的多光谱数据绘制研究区的组成岩性单元。这些数据的处理基于一种精确的方法，该方法区分并突出了具有近似光谱特征相似性的不同岩性单元的限制。三种技术用于增强图像，包括主成分分析 (PCA)、最小噪声分数 (MNF)、和独立成分分析（ICA）。岩性映射使用两种类型的监督分类进行：最大似然分类器 (MLC) 和支持向量机 (SVM)。处理数据的结果表明，Sentinel 2A 数据在岩性单元制图方面的有效性优于 ASTER 和 Landsat OLI 数据。对 Sentinel 2A MSI 图像的两种方法的分类评估表明，SVM 方法提供了更好的分类，总体准确率为 93.93%，Kappa 系数为 0.93，而 MLC 方法的总体准确率为 82.86%和 0.80 的 Kappa 系数。测绘结果与研究区地质图具有良好的相关性，遥感识别中央高图集不同岩性单元的效率较高。