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Joint Multiview Fused ELM Learning with Propagation Filter for Hyperspectral Image Classification

  • Yu ShenEmail author
  • Liang XiaoEmail author
  • Mohsen MolaeiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

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.

Keywords

Extreme Learning Machine Hyperspectral Image Propagation Filter Overall Accuracy Decision Level Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported in part by the National Key Research and Development Program of China under Grant No. 2016YFF0103604, by the National Natural Science Foundation of China under Grant No. 61571230, Grant No. 11431015 and Grant No. 61171165; by the National Scientific Equipment Developing Project of China under Grant No. 2012YQ050250; and by the Natural Science Foundation of Jiangsu Province under Grant No. BK20161500.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNanjingChina

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