研究目的
To improve machine generalization in hyperspectral image classification by developing a semisupervised active learning method that uses pseudolabeled samples selected automatically and actively, incorporating superpixel-based spatial adaptivity and density-peak-based augmentation.
研究成果
The proposed superpixel-based semisupervised active learning method effectively improves hyperspectral image classification by selecting confident and informative pseudolabeled samples, demonstrating robustness across different datasets and outperforming comparative methods in terms of accuracy and sample efficiency.
研究不足
The method may be limited by the computational complexity of handling large datasets and the dependence on the quality of superpixel segmentation. The augmentation strategy might not fully address all unlabeled superpixels in very large images.
1：Experimental Design and Method Selection:
The study employs a semisupervised active learning framework integrating superpixel-based spatial assumptions and density-peak clustering for pseudolabel augmentation. It uses multinomial logistic regression with a Markov random field regularizer for classification.
2：Sample Selection and Data Sources:
Three real hyperspectral datasets are used: AVIRIS Indian Pines, ROSIS Pavia University, and OMIS Zaoyuan scene, with ground-truth maps for validation.
3：List of Experimental Equipment and Materials:
Hyperspectral sensors (AVIRIS, ROSIS, OMIS) for data acquisition; no specific lab equipment mentioned.
4：Experimental Procedures and Operational Workflow:
The method involves generating candidate pseudolabeled samples using superpixel-based confidence criteria, applying breaking ties active learning for informativeness, and iteratively training the classifier.
5：Data Analysis Methods:
Performance is evaluated using overall accuracy, average accuracy, class individual accuracy, kappa statistic, and statistical significance via Monte Carlo runs.
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