Abstract
Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.
Highlights
Lithology is closely related to many important issues such as geological disasters, mineral storage, and oil reservoirs
Machine learning algorithms (MLAs), a sub-domain of artificial intelligence, aim to automatically extract information from data, through statistical or non-probabilistic approaches. This classification technique is divided into two types: (i) unsupervised classification, which classifies the rock type based solely on the spectral information without being assisted by training zones and without the process resulting in spectral clustering by an iterative technique [19,22,23,24]; and (ii) supervised classification, which consists of assigning groups of identical pixels to classes that correspond to each type of rock by comparing the pixels with each other and with those whose lithology is known
Support vector machines (SVM) was applied to perform an automated lithological classification of the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier located in southern Morocco (Western Anti-Atlas) using remote-sensing data, namely Landsat 8 Operational Land Imager (OLI) data and a digital elevation model (DEM) of ALOS/PALSAR with 12.5-m spatial resolution
Summary
Lithology is closely related to many important issues such as geological disasters, mineral storage, and oil reservoirs. Spectral data from space and airborne sensors were widely applied to geological mapping, including lithological discrimination [1,2,3,4,5,6,7,8], structural mapping [9], hydrothermal alteration [10,11,12], and economic mineral deposits [13,14,15,16,17] Because of their cost effectiveness, especially in mapping inaccessible areas [4,18,19,20] and in the production of small-scale maps, remote-sensing methods provide a good alternative to traditional field work [21]. This work was carried out in five stages: (i) data preprocessing, (ii) visual interpretation of different lithological units, (iii) automatic lithological mapping by SVM, (iv) accuracy evaluation, and (v) assessment
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