Cao Faxian, Yang Zhijing, Ren Jinchang, Jiang Mengying, Ling Wing-Kuen
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.
Sensors (Basel). 2017 Nov 13;17(11):2603. doi: 10.3390/s17112603.
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.
作为一种新的机器学习方法，极限学习机（ELM）因其良好的性能受到了广泛关注。然而，直接应用于高光谱图像（HSI）分类时，识别率较低。这是因为ELM没有利用空间信息，而空间信息对HSI分类非常重要。鉴于此，本文提出了一种将ELM与循环置信传播（LBP）相结合的HSI光谱-空间分类新框架。原始的ELM是线性的，非线性ELM（或核ELM）是对线性ELM（LELM）的改进。然而，通过大量实验和分析发现，在HSI的光谱-空间分类中，LELM比非线性ELM是更好的选择。此外，我们利用了使用HSI中全部信息的边际概率分布，并使用LBP学习这种分布。所提方法不仅保持了ELM的快速性，而且大大提高了分类精度。在著名的HSI数据集印度松树和帕维亚大学上的实验结果证明了所提方法的良好性能。
