期刊
REMOTE SENSING
卷 13, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/rs13020193
关键词
semi-supervised learning; graph construction; label propagation; particle competition and cooperation; hyperspectral image classification
类别
资金
- National Natural Science Foundation of China [U1813222]
- Tianjin Natural Science Foundation [18JCYBJC16500]
- Key Research and Development Project from Hebei Province [19210404D]
- Other Commissions Project of Beijing [Q6025001202001]
This study introduces a novel graph-based semi-supervised algorithm incorporating particle cooperation and competition, which effectively improves model performance, reduces label noise, and enhances classification performance.
Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples. First, for the purpose of reducing the generation of label noise, we used an efficient constrained graph construction approach to calculate the affinity matrix, which is capable of constructing a highly correlated similarity relationship between the graph and the samples. Then, we introduced a particle competition and cooperation mechanism into label propagation, which could detect and re-label misclassified samples dynamically, thus stopping the propagation of wrong labels and allowing the overall model to obtain better classification performance by using predicted labeled samples. Finally, we applied the proposed model into hyperspectral image classification. The experiments used three real hyperspectral datasets to verify and evaluate the performance of our proposal. From the obtained results on three public datasets, our proposal shows great hyperspectral image classification performance when compared to traditional graph-based SSL algorithms.
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