Semi-supervised class-specific feature selection for VHR remote sensing images
Semi-supervised class-specific feature selection for VHR remote sensing images
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DOI：
10.1080/2150704x.2016.1171923
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发表时间：
2016-04
影响因子：
2.3
通讯作者：
X. Chen;Gongjian Zhou;H. Qi;G. Shao;Yanfeng Gu
中科院分区：
文献类型：
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作者：
X. Chen;Gongjian Zhou;H. Qi;G. Shao;Yanfeng Gu
与主题类相关的抽象特征，即类特有特征，有利于主题信息的提取。然而，现有的特定类别特征选择方法需要大量的标记样本，而样本标记往往费时费力。因此，有必要选择标记对象不足的特定于类的要素。在本文中，我们提出了半监督类特定特征选择问题，并提出了一种新的两阶段方法。首先，权重矩阵充分融合了局部几何结构和判别信息。其次，将权重矩阵引入数据重构的a范数最小化优化问题中，以客观地衡量特征对主题类的有效性。与标签向量的显式二值化不同，新方法只在权值矩阵中隐式二值化。在接收器工作特性曲线下面积的情况下，类特定特征导致贝叶斯和线性支持向量机的平均分别增加3%和4%。
ABSTRACT Features relevant to a thematic class, that is, class-specific features are beneficial to thematic information extraction. However, existing class-specific feature selection methods require abundant labelled samples, while sample labelling is always labour intensive and time consuming. Therefore, it is necessary to select class-specific features with insufficient labelled objects. In this paper, we raise this problem as semi-supervised class-specific feature selection and propose a new two-stage method. First, a weight matrix fully integrates local geometrical structure and discriminative information. Second, the weight matrix is incorporated into a -norm minimization optimization problem of data reconstruction to objectively measure the effectiveness of features for a thematic class. Different from the explicit binarization in the label vector, the new method only implicitly employs binarization in the weight matrix. With area under receiver-operating characteristic curve, class-specific features result in an increase from 3% and 4% on average for Bayes and linear support vector machine, respectively.