摘要: In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features... 展开 In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features are generated from the hyperspectral image by using a multiscale spectral-spatial context aware propagation filter. We next apply the weighted-based probabilistic ELM to these multiple feature views to obtain a robust supervised classification results with high accuracy. The advantages of the proposed method are twofold: (1) the multiscale local spectral-spatial contexts of the image are able to be exploited to improve the classification performance significantly; and (2) the algorithm is simple but very robust to the small size of training labeled samples. The experimental results suggest that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based classifiers and the classical SVM classifier. 收起