Convolutional neural network extreme learning machine for effective classification of hyperspectral images

Faxian Cao; Zhijing Yang; Jinchang Ren; Bingo Wing-Kuen Ling

doi:10.1117/1.JRS.12.035003

5 July 2018 Convolutional neural network extreme learning machine for effective classification of hyperspectral images

Faxian Cao, Zhijing Yang, Jinchang Ren, Bingo Wing-Kuen Ling

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 3, 035003 (July 2018). https://doi.org/10.1117/1.JRS.12.035003

Abstract

Due to its excellent performance in terms of fast implementation, strong generalization capability, and straightforward solution, extreme learning machine (ELM) has attracted increasing attention 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 a 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 while 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 datasets, 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.

Citation Download Citation

Faxian Cao, Zhijing Yang, Jinchang Ren, and Bingo Wing-Kuen Ling "Convolutional neural network extreme learning machine for effective classification of hyperspectral images," Journal of Applied Remote Sensing 12(3), 035003 (5 July 2018). https://doi.org/10.1117/1.JRS.12.035003

Received: 30 March 2018; Accepted: 8 June 2018; Published: 5 July 2018

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