Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

Brian Bino Sinaice,Youhei Kawamura,Mahdi Saadat,Zibisani Bagai,Narihiro Owada,Fumiaki Inagaki,Hisatoshi Toriya

doi:10.3390/min11080846

Brian Bino Sinaice, Youhei Kawamura + Show 5 more

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https://doi.org/10.3390/min11080846

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Abstract

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system.

Highlights

Introduction published maps and institutional affilThe adoption of advanced automated technology into various industries has proven to be highly effective in improving sustainability and efficiencies
Through dimensionality reduction (DR), we can reduce the storage capacity required to store and handle a database, thereby reducing storage costs as we have proven there is no need to collect, store and process redundant data; With DR, we were able to break down hyperspectral signature data into different dimensionalities, the ability to plot such data in 2D planes, which as a result allows for easy visual assessment; As proven with post-Neighbourhood Component Analysis (NCA) specialised multispectral imaging, we can attain respectable classification accuracies
This paper proposes the combination and DR of hyperspectral data via NCA to multispectral imaging, coupled with Machine Learning (ML) as a method by which subsequent spectral characteristics of rocks, minerals and the environment can be performed without unnecessary processing of redundant data

Summary

Introduction

The adoption of advanced automated technology into various industries has proven to be highly effective in improving sustainability and efficiencies This is greatly due to the optimisation of system designs, data collection methods and the overall implementation of automation. This industry strives for the improvement of safety regulations via increasing the distance between miners and the environment [1] This is where automated technology plays its part, by improving site data collection methods followed by high accuracy analysis methods [2]. One of such improvements has been demonstrated by researchers [2,3], where they employed hyperspectral signatures of rocks and a neural network to classify rocks based on their spectral signatures. Though the hundreds of spectral bands in hyperspectral imaging iations

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