Application of Hierarchical Extreme Learning Machine in Prediction of Insulator Pollution Degree Using Hyperspectral Images

Hyperspectral images possess merging properties of image and spectrum, wide spectral range, and high spectral resolution, which can finely reflect the material microscopic characteristics. To this end, hyperspectral imaging technology is introduced to research the insulator pollution degree in a non-contact way. Considering that extreme learning machine (ELM) has high learning efficiency and strong generalization ability, we construct a ELM with regularization constraint based insulator pollution degree prediction (ELM-IPDP) model. Besides, in order to further improve the prediction performance, hierarchical ELM (HELM) is utilized to learn the effective, abstract, and discriminative feature representations from the complex hyperspectral images, and the HELM based insulator pollution degree prediction (HELM-IPDP) model is proposed. Experiments are performed with different amounts of training data and numbers of neurons in hidden layers. Experimental results show that the prediction performance is basically improved with the increase of numbers of neurons in hidden layer and training samples. Specifically, when the proportion of training sample and test sample is 9∶1, root mean squared error (RMSE) and correlation coefficient of the ELM-IPDP model are 0.040 3 and 0.944 7, while those of the HELM-IPDP model are up to 0.022 3 and 0.972 0, respectively.