A stacked ensemble learning-based framework for mineral mapping using AVIRIS-NG hyperspectral image

Abstract

Hyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at each pixel. This data can be used in numerous remote sensing (RS) applications, along with mineral mapping. Mineral mapping is an important component of geological mapping, which helps in investigating the mineralization potential of an area. This work can be completed effectively by applying machine learning (ML) techniques to RS data. This paper proposes a stacked ensemble-based framework for mineral mapping using the dataset obtained by the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). The study area is situated in Jahazpur, Rajasthan, India. The purpose of this stacked ensemble-based model is to enhance the performance of ML-based mineral mapping. The proposed stacked ensemble model consists of two major elements: a base learner (Naïve Bayes, KNN, artificial neural network, decision tree, and support vector machine) and a stacked learner (random forest). The results of the experiments show that the stacked ensemble-based model has a lot of potential for accurately mapping the minerals talc, montmorillonite, kaolionite, and kaosmec. The proposed model has obtained an overall accuracy of 98.96%, an average accuracy of 98.21%, and a Kappa coefficient of 0.9628.

Research highlights

• A stacked ensemble-based model for mineral mapping is proposed.

• The well-known five conventional machine learning models (called base models) are investigated for mineral mapping.

• The performance of the proposed model is evaluated on the AVIRIG–NG dataset. The study area is situated in Jahazpur, Rajasthan, India.

• The proposed method outperformed all base models.