Efficient feature selection and nearest neighbour search for hyperspectral image classification

Woodley, Alan, Chappell, Timothy, Geva, Shlomo, Nayak, Richi, & (2016) Efficient feature selection and nearest neighbour search for hyperspectral image classification. In Liew, Alan Wee-Chung, Lovell, Brian C., Fookes, Clinton B., Zhou, Jun, Goa, Yongsheng, Blumenstein, Michael, et al. (Eds.) 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE (Institute of Electrical and Electronics Engineers), Gold Coast, QLD.

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Hyperspectral images typically contain hundreds of spectral bands, which is one to two orders of magnitude larger than the number of bands in multispectral images. This greater volume of spectral information could lead to discoveries that are not possible with multispectral images; however, overcoming the complexity of the additional information is a computational challenge. Here, we present a solution that uses feature selection, logarithmic nearest neighbor classification and neighborhood spatial analysis to classify the land use of multiple hyperspectral images. Empirical analysis shows that our solution is as accurate as other much more complex approaches and it is orders-of-magnitude more efficient. This ascertains that our solution is scalable to larger datasets while maintaining high accuracy.

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ID Code: 104530
Item Type: Conference Paper
Refereed: Yes
Keywords: hyperspectural; remote sensing; feature selection; nearest neighbour; loonne, k-tree
DOI: 10.1109/DICTA.2016.7797035
ISBN: 9781509028962
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: 2016 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 16 Mar 2017 00:37
Last Modified: 30 Jun 2017 11:58

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