Semisupervised Neural Networks for Efficient Hyperspectral Image Classification
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification
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DOI：
10.1109/tgrs.2009.2037898
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发表时间：
2010-02
影响因子：
8.2
通讯作者：
F. Ratle;Gustau Camps-Valls;J. Weston
中科院分区：
文献类型：
--
作者：
F. Ratle;Gustau Camps-Valls;J. Weston
提出了一种基于神经网络的半监督遥感图像分类框架。该方法包括添加一个灵活的嵌入正则化用于训练神经网络的损失函数。训练是使用随机梯度下降与额外的平衡约束，以避免陷入局部最小值。该方法构成了监督和无监督方法的推广，可以处理数百万个未标记的样本。因此，所提出的方法产生了一个操作分类器，而不是以前提出的转导或拉普拉斯支持向量机（TSVM或LapSVM，分别）。所提出的方法构成了一个通用的框架，用于构建计算效率高的半监督方法。该方法相比，LapSVM和TSVM在半监督的情况下，SVM在监督设置，和在线和批量k-均值无监督学习。实验结果表明，该方法在高光谱图像分类问题上提高了分类精度和可扩展性。
A framework for semisupervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regularizer to the loss function used for training neural networks. Training is done using stochastic gradient descent with additional balancing constraints to avoid falling into local minima. The method constitutes a generalization of both supervised and unsupervised methods and can handle millions of unlabeled samples. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented transductive or Laplacian support vector machines (TSVM or LapSVM, respectively). The proposed methodology constitutes a general framework for building computationally efficient semisupervised methods. The method is compared with LapSVM and TSVM in semisupervised scenarios, to SVM in supervised settings, and to online and batch k-means for unsupervised learning. Results demonstrate the improved classification accuracy and scalability of this approach on several hyperspectral image classification problems.