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Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

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Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116085, China
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Sensors 2018, 18(11), 3601; https://doi.org/10.3390/s18113601
Received: 8 September 2018 / Revised: 16 October 2018 / Accepted: 19 October 2018 / Published: 23 October 2018
(This article belongs to the Section Remote Sensors)
Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method. View Full-Text
Keywords: hyperspectral image classification; rotation forest; extreme learning machine; Q-statistic hyperspectral image classification; rotation forest; extreme learning machine; Q-statistic
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Lv, F.; Han, M. Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm. Sensors 2018, 18, 3601.

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