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.
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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||hyperspectural; remote sensing; feature selection; nearest neighbour; loonne, k-tree|
|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:||21 Mar 2017 18:33|
Repository Staff Only: item control page