As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.
- hyperspectral image (HSI)
- extreme learning machine (ELM)
- spectral-spatial image classification
- discriminative random field (DRF)
- loopy belief propagation (LBP)
Cao, F., Yang, Z., Ren, J., Jiang, M., & Ling, W-K. (2017). Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors, 17(11). https://doi.org/10.3390/s17112603