| Citation: | GAO Fei, ZHU Fuli. Semi-supervised classification based on class certainty of samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1941-1951. doi: 10.13700/j.bh.1001-5965.2017.0708(in Chinese)
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The performance of supervised learning based algorithms can decrease dramatically in the classification of remote sensing images if labeled samples are insufficient. The collection of labeled samples is generally time-consuming and expensive, though unlabeled samples can be relatively easily obtained. To utilize the information of unlabeled samples in the learning process, this paper proposes a novel semi-supervised classification algorithm based on class certainty of samples (CCS). First, a multi-class support vector machine (SVM) is employed to determine the class certainty of unlabeled samples, which effectively measure the class reliability of unlabeled samples. Then, the pre-processing of sample classification is carried out to enhance the security of unlabeled samples. Finally, a new semi-supervised linear discriminant analysis (LDA) is proposed based on the sample class certainty and results in improved separability of the samples in the projection subspace. Moreover, the semi-supervised LDA can be extended to nonlinear dimensional reduction by combining the class certainty and the kernel based methods. For classification of the testing samples, the nearest neighbor classifier is adopted. In order to assess the effectiveness of the proposed algorithm, several experiments are carried out on the actual synthetic aperture radar (SAR) images in comparison with other supervised and semi-supervised algorithms. Using real SAR images, it is proved that the proposed algorithm is superior to all supervised and other semi supervised algorithms in the case of less marked samples. And it can converge quickly.
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