Hyperspectral image classification using comprehensive evaluation model of extreme learning machine based on cumulative variation weights

Abstract

In order to improve the classification of hyperspectral image(HSI), we propose a novel hyperspectral image classification method based on the comprehensive evaluation model of extreme learning machine(ELM) with the cumulative variation weights(CVW), referred to as ELM with the cumulative variation weights and comprehensive evaluation (CVW-CEELM). To be specific, the cumulative variation value is proposed as a new metric. The inefficient bands are eliminated by the cumulative variation quotient values based on the cumulative variation values. The cumulative variation weights based on the cumulative variation values are used to determine the contribution of each weak ELM classifier to the hyperspectral image classification algorithm. The remaining effective bands are divided by grouping strategy. In each group of the effective bands, the different numbers of bands are selected to reduce the dimension of the hyperspectral image dataset by the weighted random-selecting-based method. After dimensionality reduction, the spatial-spectral features of each pixel are extracted and multiple weak ELM classifiers are trained by the training samples. Then, the results of several weak classifiers are synthetically evaluated by the cumulative variation weights to get the final classification results. Experimental results on the typical hyperspectral image datasets illustrate that the proposed CVW-CEELM has few adjustable parameters to make the operation simple, and outperforms a variety of the image classification counterparts in terms of the calculation cost and classification accuracy.