Convolutional neural network extreme learning machine for effective classification of hyperspectral images
Cao, Faxian and Yang, Zhijing and Ren, Jinchang and Ling, Bingo Wing-Kuen (2018) Convolutional neural network extreme learning machine for effective classification of hyperspectral images. Journal of Applied Remote Sensing, 12 (3). 035003. ISSN 1931-3195 (https://doi.org/10.1117/1.JRS.12.035003)
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Abstract
Due to its excellent performance in terms of fast implementation, strong generalization capability and straightforward solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) classification. However, the performance of ELM for HSI classification remains a challenging problem especially in effective extraction of the featured information from the massive volume of data. To this end, we propose in this paper a new method to combine Convolutional neural network (CNN) with ELM (CNN-ELM) for HSI classification. As CNN has been successfully applied for feature extraction in different applications, the combined CNN-ELM approach aims to take advantages of these two techniques for improved classification of HSI. By preserving the spatial features whilst reconstructing the spectral features of HSI, the proposed CNN-ELM method can significantly improve the accuracy of HSI classification without increasing the computational complexity. Comprehensive experiments using three publicly available HSI data sets, Pavia University, Pavia center, and Salinas have fully validated the improved performance of the proposed method when benchmarking with several state-of-the-art approaches.
ORCID iDs
Cao, Faxian, Yang, Zhijing, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194 and Ling, Bingo Wing-Kuen;-
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Item type: Article ID code: 64512 Dates: DateEvent5 July 2018Published8 June 2018AcceptedNotes: © 2018 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 18 Jun 2018 13:12 Last modified: 14 Oct 2024 00:33 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/64512