Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

Abstract

As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification.

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

“…MLAs are attracting increasing interest in the field of remote sensing as an approach to geological mapping. Although multispectral and hyperspectral data are widely used for lithological discrimination and classification, it is difficult to obtain appropriate data for geological mapping because of the high cost and complexity of the treatment [26]. Therefore, the combination of multispectral data with textural data and lithological classification characteristics is very effective in achieving good results.…”

“…According to Yang [68], RBF-SVM represents the best kernel type when the penalty parameter is at 100 and the gamma parameter in the kernel function is the inverse of the band numbers in the input. In the present work, the penalty parameter was set to 100, and the gamma parameter in the kernel function was the inverse of the band number of the Landsat OLI DEM dataset, i.e., 0.16 [26,35]. The RBF-SVM was performed in SAGA GIS 6.3.0 software and all data were scaled between −1 and 1 prior to their input into the SVM.…”

“…The number of hidden layer neurons was 1. The training rate field and the training root-mean-square (RMS) exit criteria were set to 0.2 and 0.1, respectively [26].…”

“…The advances in supervised image classification techniques based on MLAs improved geological studies using remote-sensing data. The classification algorithms that were successfully applied in geological mapping include the maximum likelihood classifier (MLC) [25,26], naïve Bayes (NB) [27,28], artificial neural networks (ANNs) [27,[29][30][31], k-nearest neighbors (K-NN) [26,27], and random forests (RF) [29,32]. In this study, an image classification algorithm considering support vector machines (SVMs) is used as an empirical method (a data-driven technique).…”

Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

“…Multispectral imagery data produced by multispectral sensors such as Landsat 8 OLI, Terra ASTER, Landsat 5 TM, Sentinel-2A and Landsat 7 ETM + have shown their efficiency in mapping lithological units at different scales [2] [4], as well as hyperspectral imagery data from hyperspectral satellite and airborne sensors such as EO-1 Hyperion and AVIRIS respectively, which are used for lithological and mineralogical mapping due to their high spectral resolution allowing the identification of hydrothermal alterations minerals [3], [6], [7]. Several studies have been achieved in lithological mapping by applying different spectral and radiometric techniques and methods in order to enhance the lithological structures [8], [9]. In addition, radar remote sensing has been widely used in geological mapping as it is extremely operational in the acquisition of images day and night and even below clouds [10], [11].…”