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

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## Abstract

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## 1. Introduction

## 2. Methodology for Coupling NCA Dimensionality Reduction with Machine Learning

#### 2.1. Hyperspectral Imaging

#### 2.2. Dimensionality Reduction

#### 2.2.1. Supervised vs. Unsupervised Methods

#### 2.2.2. Why Use NCA

#### 2.3. Why Machine Learning?

## 3. Practical Experiments

#### 3.1. Capturing Rock Hyperspectral Signatures

#### 3.2. Selecting the Appropriate Feature Bands

#### 3.3. Post-NCA Classification via ML

## 4. Experimental and Analytical Results

#### 4.1. Findings Based on Hyperspectral Imaging

#### 4.2. Findings Based on NCA

#### 4.3. Classification with ML, Post-NCA

## 5. Significance of Proposed System

- Through 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, hence the ability to plot such data in 2D planes, which as a result allows for easy visual assessment;
- As proven with post-NCA specialised multispectral imaging, we can attain respectable classification accuracies. This proves that multispectral imaging is a good enough option as it can be programmed to be highly specialised, costs less, has lower operation costs, has the flexibility of being applied in specialised multispectral imaging, such as on a UAV drone. Having said this, it is important to note that samples used in this study were clean and manually prepared before analysis, which is not the state in which rocks are found in the field, due to dirt and other matter. Therefore, classification accuracy variations in our envisioned identification of these rocks in the field, compared to the study’s attained results, are likely to exist;
- Through ML, we can analyse and classify multispectral signatures produced by rocks and minerals with high accuracies. By finding the right model for a particular dataset, subsequent related data is relatively easier to classify as the training data always assists the model in future predictions as proven;
- With our proposed combined system, we have proved that any industry looking to cut spectral data (or equivalent) analysis costs whilst still retaining high classification accuracies, DR via a feature selection supervised NCA algorithm to specify the most discriminative bands, and verifying the viability of selected bands via ML, thereafter employing these 5-bands (or more, depending on application) in future specialised classifications, could potentially be the key to achieving several system design optimisations;
- Via a post-NCA 5-band rock and mineral classification specialised multispectral camera mounted on a UVA drone, such as the ‘DJI P4 Multispectral drone used in agricultural applications’, there is a plethora of applications in which this specialised technology could find potential use. This, as a result, minimises purchase, operation and data interpretation costs as compared to a hyperspectral imaging system. This could aid in remote sensing from long distances without the need for physical presence, as well as rapid in situ assessments of the state of the environment via the UAV drone, possibilities are endless.
- Lastly, there is potential to employ such a post-NCA specialised multispectral camera in the frequent monitoring of mine dams. This would allow quicker assessment of contaminants based on spectral signatures produced by unexpected and/or anticipated metal contaminants. Hence, we deem this proposed system viable in all mining-related stages, from exploration, operation and closure.

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Summary of rock spectral reflectance strength data accumulation and preprocessing prior to dimensionality reduction via Neighbourhood Component Analysis and Machine Learning (a continuation from Figure 5).

**Figure A2.**Relative scatter and histogram plots of rock spectral reflectance strengths in 2D plane projections, where bands 441 nm is the x-axis, and bands 535 nm, 741 nm, 791 nm and 897 nm are the y-axis. The position of each point (spectra) is based on the relative intensities between bands, as well as between rocks.

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**Figure 1.**Our proposed system design, consisting of hyperspectral imaging, dimensionality reduction via Neighbourhood Component Analysis, Machine Learning classification of rocks and minerals to test system viability based on the selected features, and finally employing those features together with the ML model in developing a unmanned aerial vehicle drone-mountable multispectral camera.

**Figure 2.**Images of some of the 32 rock samples from eight rock lithologies used in building our rock hyperspectral database.

**Figure 3.**A hyperspectral signature capturing setup is used in collecting and building a rock and/or mineral spectral database. The curves are spectra in different spatial pixels of eight rock lithologies.

**Figure 4.**Automatic random extraction of rock pixel spectra captured via a Specim IQ camera that acquires 512 × 512 pixel images from 204 bands within the Visible-Near-Infrared-Range. Extracted spectra are used to build a rock hyperspectral database.

**Figure 5.**Reflectance hyperspectral signatures of eight rock lithologies employed in the construction of a hyperspectral database. Each anomaly represents the interaction between each 20 × 20 pixels 2D area with a depth of 204 bands within a rock’s hyperspectral image with light, captured via a Visible-Near-Infrared-Range hyperspectral camera.

**Figure 6.**Neighbourhood Component Analysis feature selection which assigns higher weights to the most discriminatory hyperspectral bands by eliminating redundancy in data, with the top five feature-bands being 14, 46, 116, 133 and 169.

**Figure 7.**Projection of complex rock hyperspectral signature data points in dimensionality reduced (via Neighbourhood Component Analysis feature selection) 2D planes showing the relative reflectance spectral strength relationships between five different spectral bands for eight rock lithologies.

**Figure 8.**Database handling of training and testing sets. A 5-fold-cross-validation was used in all instances.

**Figure 9.**Confusion matrix from a Cubic SVM machine learning model used in evaluating the classification viability of post dimensionality reduction spectral bands.

**Table 1.**Top five machine learning classification comparisons based on predimensionality reduction from 204-bands, to postdimensionality reduction (using Neighbourhood Component Analysis) for 100, 50, 25, 10 and 5 rock spectral bands.

Number of Classification Bands Post-NCA | Machine Learning Algorithm | Global Accuracy (%) | Average per-Class Precision (%) | Training Time (s) |
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204-bands ^{1} | SVM (Cubic SVM) | 90.7 | 90.0 | 28.7 |

SVM (Quadratic SVM) | 87.0 | 86.0 | 27.3 | |

SVM (Linear SVM) | 79.1 | 76.5 | 13.8 | |

Linear discriminant | 80.4 | 78.4 | 4.6 | |

Ensemble (Subspace discriminant) | 81.2 | 79.3 | 41.5 | |

100-bands | SVM (Cubic SVM) | 89.4 | 88.7 | 37.7 |

SVM (Quadratic SVM) | 84.7 | 83.8 | 21.8 | |

Quadratic Discriminant | 77.9 | 77.5 | 1.0 | |

SVM (Linear SVM) | 76.9 | 76.0 | 5.9 | |

Linear Discriminant | 76.7 | 75.6 | 1.1 | |

50-bands | SVM (Cubic SVM) | 86.9 | 85.7 | 39.1 |

SVM (Quadratic SVM) | 84.3 | 82.1 | 23.2 | |

Quadratic Discriminant | 79.1 | 78.9 | 1.1 | |

SVM (Linear SVM) | 76.1 | 75.4 | 5.3 | |

Ensemble (Subspace KNN) | 75.2 | 75.0 | 34.9 | |

25-bands | SVM (Cubic SVM) | 86.3 | 86.2 | 45.1 |

SVM (Quadratic SVM) | 83.9 | 82.6 | 28.2 | |

SVM (Fine Gaussian SVM) | 75.9 | 70.6 | 6.7 | |

Quadratic Discriminant | 75.7 | 75.3 | 1.2 | |

Ensemble (Subspace KNN) | 75.4 | 70.0 | 29.2 | |

10-bands | SVM (Cubic SVM) | 81.0 | 80.3 | 78.2 |

SVM (Quadratic SVM) | 78.2 | 76.0 | 40.7 | |

SVM (Fine Gaussian SVM) | 72.7 | 70.1 | 8.2 | |

Ensemble (Bagged tress) | 71.0 | 69.8 | 19.1 | |

Ensemble (Subspace KNN) | 70.6 | 70.3 | 17.9 | |

5-bands | SVM (Cubic SVM) | 70.9 | 72.0 | 182.1 |

Ensemble (Bagged trees) | 68.6 | 67.0 | 12.3 | |

SVM (Quadratic SVM) | 68.4 | 65.8 | 76.7 | |

SVM (Fine Gaussian SVM) | 68.4 | 66.6 | 7.5 | |

KNN (Fine KNN) | 67.3 | 66.0 | 5.7 |

^{1}Predimensionality reduction.

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**MDPI and ACS Style**

Sinaice, B.B.; Owada, N.; Saadat, M.; Toriya, H.; Inagaki, F.; Bagai, Z.; Kawamura, Y.
Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems. *Minerals* **2021**, *11*, 846.
https://doi.org/10.3390/min11080846

**AMA Style**

Sinaice BB, Owada N, Saadat M, Toriya H, Inagaki F, Bagai Z, Kawamura Y.
Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems. *Minerals*. 2021; 11(8):846.
https://doi.org/10.3390/min11080846

**Chicago/Turabian Style**

Sinaice, Brian Bino, Narihiro Owada, Mahdi Saadat, Hisatoshi Toriya, Fumiaki Inagaki, Zibisani Bagai, and Youhei Kawamura.
2021. "Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems" *Minerals* 11, no. 8: 846.
https://doi.org/10.3390/min11080846