Abstract
Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhu, X.X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., Fraundorfer, F.: Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Mag. 5(4), 8–36 (2017)
Sawant, S.S., Prabukumar, M.: Semi-supervised techniques based hyper-spectral image classification: a survey. In: Power and Advanced Computing Technologies (i-PACT), 2017 Innovations in IEEE, pp. 1–8 (2017)
Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral–spatial feature learning via deep residual conv–deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(1), 391–406 (2018)
Villa, A., Chanussot, J., Benediktsson, J.A., Jutten, C., Dambreville, R.: Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recogn. 46(6), 1556–1568 (2013)
Satpathy, R., Singh, V.K., Parveen, R., Jeyaseelan, A.T.: Spectral analysis of hyperion data for mapping the spatial variation of AL + OH minerals in a part of Latehar & Gumla district, Jharkhand. J. Geogr. Inf. Syst. 2(4), 210 (2010)
Vigneshkumar, M., Yarakkula, K.: Nontronite mineral identification in Nilgiri hills of Tamil Nadu using hyperspectral remote sensing. In: IOP Conference Series: Materials Science and Engineering, vol. 263, no. 3, p. 032001. IOP Publishing (2017)
Ranjan, S., Nayak, D., Satish Kumar, K., Dash, R., Majhi, B.: Hyperspectral Image Classification: A k-means Clustering Based Approach, pp. 1–7 (2017)
Kingrani, S.K., Levene, M., Zhang, D.: Estimating the number of clusters using diversity. Artif. Intell. Res. 7(1), 15 (2017)
Moughal, T.: Hyperspectral image classification using support vector machine. In: J. Phys.: Conference Series, vol. 439, no. 1, p. 012042. IOP Publishing, (2013)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, P., Venkatesan, M. (2020). Mineral Identification Using Unsupervised Classification from Hyperspectral Data. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_25
Download citation
DOI: https://doi.org/10.1007/978-981-15-0135-7_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0134-0
Online ISBN: 978-981-15-0135-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)