Unlabeled Sample Reduction in Semi-supervised Graph-Based Band Selection for Hyperspectral Image Classification
Unlabeled Sample Reduction in Semi-supervised Graph-Based Band Selection for Hyperspectral Image Classification
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DOI：
10.1109/icig.2013.88
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Rui Huang;Lisha Yang;Zhiqiang Lv
中科院分区：
文献类型：
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作者：
Rui Huang;Lisha Yang;Zhiqiang Lv
基于图的半监督波段选择方法在标记样本非常有限的情况下，对高光谱数据分类中有价值的波段选择表现出了令人满意的性能。然而，基于所有已标记和未标记样本的邻接矩阵的计算需要很大的计算量，这对于可用的大量未标记样本来说是不可接受的。针对这一问题，提出了一种无标记样本约简方法。该方法通过主成分分析进行降维，通过分水岭进行过分割，从得到的聚类中随机选择样本。对高光谱数据的波段选择和分类实验表明，通过选取具有代表性的样本，该方法有助于提高基于图的算法的计算效率和性能。
Semi-supervised graph-based band selection methods have shown satisfying performances to choose the valuable bands for the hyper spectral data classification in case of very limited labeled samples. However, the calculation of adjacency matrices based on all labeled and unlabeled samples requires a large computational load which can be unacceptable with the huge amounts of unlabeled samples available. To address the problem, an unlabeled sample reduction method is proposed. The method involves dimensional reduction through PCA, over-segmentation through watershed, random sample selection from the resulting clusters. The band selection and classification experiments on hyper spectral data demonstrate that the proposed method can help improve the computational efficiency and performances of the graph-based algorithms by choosing the representative samples.