Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

Imane Serbouti,Biswajeet Pradhan,Mohammed Raji,Chang-Wook Lee,Mustapha Hakdaoui,Abdullah M Alamri

doi:10.1109/access.2021.3107294

Abstract

Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA $\approx ~93$ %) and kappa coefficient (K) of 0, 89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0, 84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0, 80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.

Highlights

The importance of producing geological maps makes the entire branch of remote sensing science one of theThe associate editor coordinating the review of this manuscript and approving it for publication was Qiangqiang Yuan.well-established information technology in lithological mapping and mineral exploration [1]–[3]
All the approaches generated aggregations of lithologies exposed in the field of research, the most prominent difference between the thematic maps classified by the Pixel-Based Image Analysis (PBIA), Subpixel Based Image Analysis (SPBIA) and GEOBIA methods, from visual inspections, is that: the lithological map obtained using PBIA support vector machine (SVM) machine learning algorithm give more speckled regions than the other lithological maps, as shown in the Figure 9.b
It is clear in Figure 9.c that this process enabled the detection of some facies such as Limestones, marls, phosphates and the schists and mica schists, illustrated by the yellow and green circles respectively, that were mixed in PBIA method

Summary

Introduction

Well-established information technology in lithological mapping and mineral exploration [1]–[3]. It is a very popular and powerful technique for regional geological classification process, in arid and semi-arid areas [4]. I. Serbouti et al.: Pixel and Object-Based Machine Learning Classification Schemes limitations associated with traditional field-based geological mapping over vast regions [5], [6]. Recent development of multi-spectral remote sensing data such as Sentinel-2A Multi-Spectral Imagery (MSI), launched by the European Space Agency (ESA), has shown a high potential for complex geological and mineral mapping in various parts of the globe [7]–[9]

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Discussion

Conclusion