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dc.contributor.authorSinaice, Brian
dc.contributor.authorKawamura, Youhei
dc.contributor.authorKim, Jaewon
dc.contributor.authorOkada, Natsuo
dc.contributor.authorKitahara, Itaru
dc.contributor.authorJang, Hyong Doo
dc.date.accessioned2019-12-12T03:51:44Z
dc.date.available2019-12-12T03:51:44Z
dc.date.issued2019
dc.identifier.citationSinaice, B.B. and Kawamura, Y. and Kim, J. and Okada, N. and Kitahara, I. and Jang, H.D. 2019. Application of deep learning approaches in igneous rock hyperspectral imaging. In: 28th International Symposium on Mine Planning & Equipment Selection, 2nd Dec 2019, Perth, Australia.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/77319
dc.description.abstract

Hyperspectral imaging has been applied in remote sensing amongst other disciplines, success in these has triggered its extensive use. Hence, it comes as no surprise that we took advantage of this technology by conducting a study aimed at the spectral analysis of several igneous rocks, and to deduce the spectral signatures of each rock unit using neural networks. Through visual observations and comparisons of these spectral signatures, parameters such as band curvature (shape), tilt (position) and strength were used for lithological discrimination. Even with this said, there often exists similarities in rocks, which are rather difficult to differentiate by means of visual or graphical analysis. However, with numerous technologies making new waves in today’s era and artificial intelligence (AI) being at the forefront of these developments, it was best fitting to employ deep learning, often referred to as a subset of AI; to train/learn from these hyperspectral signatures with a goal aimed at classifying these rocks. Deep learning has networks such as the convolution neural network (CNN), which has algorithms that excel in feature representation from visual imagery; taking into account that the more data is fed into the training process and later used as a database for further training, the higher the future prediction accuracy. Gathered outcomes from the CNN show exceptionally high prediction accuracy capabilities of 96%; suggesting viable field and laboratory usage of these systems as a unit for mining and rock engineering applications.

dc.publisherSpringer
dc.relation.uri10.1007/978-3-030-33954-8_29
dc.subject0914 - Resources Engineering and Extractive Metallurgy
dc.titleApplication of deep learning approaches in igneous rock hyperspectral imaging
dc.typeConference Paper
dcterms.source.startPage228
dcterms.source.endPage235
dcterms.source.titleProceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES2019
dcterms.source.conference28th International Symposium on Mine Planning & Equipment Selection
dcterms.source.conference-start-date2 Dec 2019
dcterms.source.conferencelocationPerth
dc.date.updated2019-12-12T03:51:43Z
curtin.departmentWASM: Minerals, Energy and Chemical Engineering
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidJang, Hyong Doo [0000-0002-3978-5840]
dcterms.source.conference-end-date4 Dec 2019
curtin.contributor.scopusauthoridJang, Hyong Doo [55797412200]


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