Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

Abstract

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

“…Yet, each of them shows some strengths and weakness [34]. Although the index-based approach allows us to reduce the amounts of components and to classify a large area in a short time, several indices must be applied to detect the different LC/LU classes since each of them is aimed at distinguishing just one category [35]; for instance, vegetation indices are intended to identify "green areas" and so on. ML is recognized as one of the simplest algorithms to implement and to interpret [36], but its results are not satisfying without introducing a large amount of training areas since, because of insufficient a priori information, it assumes an equal a priori probability for each land cover classes [29].…”

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

“…Over the last decades, based on various criteria, supervised and unsupervised image classification has been categorized into further groups including per-pixel and subpixel, parametric and non-parametric, soft and hard, spectral and spectral-spatial, and per-field. 25,76,77 In supervised classification, the image analyst uses training samples (known) obtained from expert knowledge to specify different spectral signatures or pixel values of the image as to different classes. 25 Based on prior knowledge, the user selects sample representative pixels of a known cover type or pattern in an image as the specific class and assign it as training sites.…”

“…Combining spectral data with ancillary information and non-statistical data in parametric approach make some difficulties for remote sensing image classification. 77 However, due to its robustness and easy implementation, the MLC is one of the most widely used parametric classification.…”

Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite

“…Lithological mapping using remote sensing data is one of the most challenging tasks in geological remote sensing given complexities involving sub-pixel-level microscopic-scale non-linear mixing of minerals and the presence of surface soil, regolith, and vegetation [1]. Hyperspectral technology combines two-dimensional imaging and spectroscopy technologies to obtain both the physiographic and spectral information of the constituent features [2].…”

“…In terms of lithological and mineral mapping, a large number of methods based on hyperspectral imagery have been proposed, which can be mainly divided into three categories: whole-pixel classification approaches, subpixel classification approaches, and machine-learning and deep-learning-based techniques [1,15]. Whole-pixel classification approaches assign an entire pixel to a single class based on features such as spectral similarity measures and derivative absorption band parameters.…”