Self-training algorithm for hyperspectral imagery classification based on mixed measurement k-nearest neighbor and support vector machine

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Self-training is commonly used for hyperspectral imagery semi-supervised learning, which selects confident unlabeled samples and puts them into the labeled set iteratively. However, self-training suffers from the influence of noises and outliers that are wrongly selected as confident samples and guide the accuracy of the classifier going down in iteration. To solve this problem, a self-training algorithm for hyperspectral imagery classification based on a mixed measurement k-nearest neighbor (k-NN) and support vector machine (SVM) framework is proposed. Different from the traditional self-training based on k-NN and SVM, the proposed method utilizes a mixed measurement k-NN to select confident unlabeled samples, and the SVM is applied to help the k-NN method to label the unlabeled samples. The mixed measurement is combined with the spectral distance, spatial distance, and local outlier factor (LOF) distance to explore the feature differences of spectra. Meanwhile, the factors of LOF and k-NN are obtained adaptively, which effectively reduces the computational complexity of the proposed method to select optimized parameters. Experimental results on five hyperspectral images show that the proposed method outperforms the competitors.