A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression

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.