Name
Title | ||
|---|---|---|
The Application Of Subspace Clustering Algorithms In Drill-Core Hyperspectral Domaining |
Abstract | ||
|---|---|---|
Diamond drilling is used in the mining industry to extract drill-cores for characterising mineral deposits. Traditionally, drill-cores are visually analysed by an on-site geologist, subjected to geochemical analyses, and then, few representative samples subjected to additional high-resolution mineralogical studies. However, the choice in samples is frequently subjective and the mineralogical analyses are highly time-consuming. In order to optimize the choice of samples and accelerate the analyses, drill-cores can be partitioned into domains, and then, laboratory analyses can be carried out on selected domains. Nevertheless, in the mining industry, automatic drill-core domaining still remains a challenge. Recently, hyperspectral imaging has become an important technique for the analysis of drill-cores in a non-invasive and non-destructive manner. Several clustering algorithms of hyper-spectral data are proposed for automatic drill-core domaining. In this paper, we suggest using advanced subspace clustering algorithms (i.e., sparse subspace clustering algorithm, spectral-spatial sparse subspace clustering algorithm). These algorithms work based on the self-representation property of the hyperspectral data. The clustering methods are tested on two drill-core samples which present different mineralogical and structural features. The subspace clustering algorithms are compared with the result of the K-means clustering algorithm. Our experimental results show that subspace clustering algorithms provide accurate drill-core domains and it is shown that including spatial information significantly improves the clustering results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WHISPERS.2019.8920854 | 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
Drill-core hyperspectral data,drill-core domaining,subspace clustering,spatial regularization,unsupervised learning | Spatial analysis,Mining industry,Subspace clustering,Computer science,Algorithm,Hyperspectral imaging,Drill,Cluster analysis,Signal processing algorithms | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-7281-5295-0 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kasra Rafiezadeh Shahi | 1 | 0 | 0.34 |
| Mahdi Khodadadzadeh | 2 | 68 | 9.12 |
| R. Tolosana-Delgado | 3 | 9 | 3.77 |
| Laura Tusa | 4 | 1 | 1.16 |
| Richard Gloaguen | 5 | 133 | 32.68 |