Semisupervised Hyperspectral Image Classification via Neighborhood Graph Learning
Semisupervised Hyperspectral Image Classification via Neighborhood Graph Learning
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DOI：
10.1109/lgrs.2015.2438227
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发表时间：
2015-06
影响因子：
4.8
通讯作者：
Daniel Jiwoong Im;Graham W. Taylor
中科院分区：
文献类型：
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作者：
Daniel Jiwoong Im;Graham W. Taylor
在标签数据稀缺的问题中，半监督学习(半监督学习)技术是一种很有吸引力的框架，可以同时利用标签数据和未标签数据。这些方法通常依赖于光滑性假设，使得在输入空间中相似的示例在标签空间中也应该相似。在许多领域，例如遥感高光谱图像分类，数据违反了这一假设。作为回应，我们提出了一种通用的方法，通过使用二进制分类器来学习SSL中使用的邻域图，二进制分类器被训练来预测一对像素是否共享相同的标签。在半监督神经网络(SSNN)的框架下，我们的方法在两个HSI数据集上改善了SSNN的性能。
In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.