In this paper, the multi-dimensional adaptive weighted filter (AWF) is used to filter the hyperspectral image with a certain dimension which are reduced by the feature extraction method based on spectral dimension. Then, the filter results obtained on all scales are hierarchical fusion into a new image, and the hierarchical fusion framework is designed. These treatments make the essential spatial and spectral features in hyperspectral images extracted effectively, so the classification accuracy is improved. The principal component analysis (PCA) algorithm is integrated into the framework, and a hierarchical fusion-principal component analysis (HF-PCA) algorithm is proposed. This method not only reduces the redundancy between bands, but also weakens the internal differences of the samples and improves the classification accuracy of hyperspectral images. Experimental results on the Indian Pines and Salinas databases demonstrate that the classification accuracy obtained by the HF-PCA algorithm is significantly higher than that of other algorithms, even when the number of training samples is small, and the maximum value of the overall classification accuracy is 86.73% and 95.01%, respectively. The classification accuracy of hyperspectral images is improved effectively.
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2019, Vol. 31 
