Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Abstract

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

“…It is based on the MODTRAN5 radiation transfer model and is currently the most accurate atmospheric correction method under ENVI software. In many studies, it has been proven to be significantly effective in removing atmospheric effects [56][57][58][59][60][61]. The fusion method used the super-resolution Bayesian fusion algorithm, namely, Gram-Schmidt Pan Sharpening, which is currently one of the best remote-sensing fusion algorithms in terms of fusion effect and color fidelity.…”

“…Minimum noise fraction (MNF) is an orthogonal transformation that is a very useful algorithm for reducing the inherent dimensionality of multispectral data and reducing noise in images [23]. MNF is essentially two cascaded principal component transformations, where the first transformation is used to separate and rescale the noise in the data, and the second transformation is the standard principal component transformation of the noisewhitened data [56]. MNF transformation is also commonly applied directly to vegetation or lithological mapping [46,69,71,79,88].…”

“…Recently, using machine learning methods in geoscience studies has been increasing in various applications, including mapping land-cover types and monitoring land usage. Some work has been carried out in the field of identifying and classifying lithologies of valuable minerals [56].…”

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

“…Their results showed a strong spatial relationship between known mineralization areas, which were mapped prospectively by a deep-learning method. Elahi et al [46] investigated the potential of two ML algorithms, including SVM and ANN, using Sentinel-2 optical data for lithological mapping in Pakistan. They reported an overall accuracy of 95.78% and 95.73% for SVM and ANN, respectively.…”