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

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≈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.

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

“…Unlike the majority of previous studies that adopted individual MLAs using thirdparty software for lithological mapping of small geographical areas [3,4,13,[126][127][128][129], we exploit the capabilities of the GEE cloud computing platform to perform the MLAs procedure using the spatio-spectral data from Sentinel 2. The spatial information was used as a pixel local variability in band measurements (entropy and contrast) contained in the texture index images extracted from the GLCMs.…”

“…Studies have shown that the SVM has advantages in terms of small samples and high dimensionality [104]. However, the fast performance and accurate results make SVM one of the widely used lithological mapping classifiers [4,105].…”

“…One of the most challenging geological applications using remote-sensing-based satellite data is mapping lithological features, especially for large and geologically complex areas that require high-precision lithological maps [1,2]. Furthermore, the limited availability of the spatial and spectral quality of open access image data (e.g., Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), ASTER and Sentinel-2) is widely utilized to extract lithological [3][4][5], mineral [6,7] and structural information [8][9][10]. The majority of studies investigating the potential of remote sensing for geological mapping [4,6,[11][12][13] have been conducted over relatively small geographical areas using individual machine learning algorithms (MLAs).…”

“…Furthermore, the limited availability of the spatial and spectral quality of open access image data (e.g., Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), ASTER and Sentinel-2) is widely utilized to extract lithological [3][4][5], mineral [6,7] and structural information [8][9][10]. The majority of studies investigating the potential of remote sensing for geological mapping [4,6,[11][12][13] have been conducted over relatively small geographical areas using individual machine learning algorithms (MLAs). Therefore, an innovative approach, based on several factors, is implemented in this research to overcome the aforementioned issues.…”

“…The application of artificial intelligence algorithms has been widely used in the field of geological applications [4,5,13]. Although many studies have attempted to compare dif-ferent algorithms to determine the best classification approach for geological mapping, no consensus on the validity of the available MLAs has yet been achieved.…”