A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression |
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| Authors: | Kun Tan Xue Wang Jishuai Zhu Jun Hu Jun Li |
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| Affiliation: | 1. Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou, China;2. Data Center Section Three, Chang Guang Satellite Technology Co., Ltd, Changchun, China;3. The First Institute of Aero-Photogrammetry and Remote Sensing, NASG, Xian, China;4. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China |
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| Abstract: | In this article, a novel active learning approach is proposed for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression/Davidon, Fletcher, and Powell selective variance (MLR-DFP-SV). The proposed approach consists of two main steps: (1) a fast solution for the MLR classifier, where the logistic regressors are obtained by the use of the quasi-Newton algorithm; and (2) selection of the most informative unlabelled samples. The SV method is applied to select the most informative unlabelled samples, based on the posterior density distributions. Experiments on two real hyperspectral data sets confirmed that the proposed approach can effectively select the most informative unlabelled samples and improve the classification accuracy. Three different methods – the maximum information (MI), breaking ties (BT), and minimum error (ME) methods – were also used to obtain the most informative unlabelled samples, and it was found that the new sample selection method – SV – can select more informative samples than the BT, MI, and ME methods. |
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