Semisupervised image classification with Laplacian support vector machines
Semisupervised image classification with Laplacian support vector machines
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DOI：
10.1109/lgrs.2008.916070
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发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Calpe, Javier
中科院分区：
文献类型：
--
作者：
Gomez-Chova, Luis;Camps-Valls, Gustavo;Calpe, Javier
提出了一种基于核机器和图论的半监督遥感图像分类方法。支持向量机（SVM）的正则化与未规范化的图拉普拉斯算子，从而导致拉普拉斯SVM（LapSVM）。该方法在城市监测和云筛选的挑战性问题进行了测试，其中充分利用未标记样本的财富是至关重要的。使用不同的传感器，并与少量的训练样本，得到的结果表明，潜在的建议LapSVM遥感图像分类。
This letter presents a semisupervised method based on kernel machines and graph theory for remote sensing image classification. The support vector machine (SVM) is regularized with the unnormalized graph Laplacian, thus leading to the Laplacian SVM (LapSVM). The method is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical. Results obtained using different sensors, and with low number of training samples, demonstrate the potential of the proposed LapSVM for remote sensing image classification.