scholarly journals Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

2014 ◽  
Vol 6 (6) ◽  
pp. 5795-5814 ◽  
Author(s):  
Chen Chen ◽  
Wei Li ◽  
Hongjun Su ◽  
Kui Liu
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Spatial Classification ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Hyperspectral image classification using FPCA-based kernel extreme learning machine

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3942-3948 ◽  
Author(s):  
Yantao Wei ◽  
Guangrun Xiao ◽  
He Deng ◽  
Hong Chen ◽  
Mingwen Tong ◽  
...  
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 2036 ◽  
Author(s):  
Jiaojiao Li ◽  
Bobo Xi ◽  
Qian Du ◽  
Rui Song ◽  
Yunsong Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Low Cost ◽  
Principal Component ◽  
Learning Machines ◽  
Spatial Features ◽  
Classification Framework ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Deep Feature Extraction

Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new classification framework derived from the deep KELM network and GFFPC is presented to generate deep spectral and spatial features. Experimental results demonstrate that the proposed framework outperforms some state-of-the-art algorithms with very low cost, which can be used for real-time processes.

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