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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jia, F., Lein, Y., Lin, J., Zhou, X., Lu, N.: Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)
Xing, J., Li, K., Hu, W., Yuan, C., Ling, H.: Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recogn. 66, 106–116 (2017)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps (2013)
Kruse, F.A.: Mapping surface mineralogy using imaging spectrometry. Geomorphology 137, 41–56 (2012)
Meer, F.: The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinf. 8, 3–17 (2006)
Pieters, C.M., et al.: The Moon Mineralogy Mapper (M3) on Chandrayaan-1. Curr. Sci. 96(4), 500–505 (2009)
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)
Tompkins, S., Pieters, C.M.: Mineralogy of the lunar crust: results from Clementine. Meteor. Planet. Sci. 34, 25–41 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sinaice, B.B., Kawamura, Y., Kim, J., Okada, N., Kitahara, I., Jang, H. (2020). Application of Deep Learning Approaches in Igneous Rock Hyperspectral Imaging. In: Topal, E. (eds) Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019. MPES 2019. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-33954-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-33954-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33953-1
Online ISBN: 978-3-030-33954-8
eBook Packages: EngineeringEngineering (R0)