Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning
Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning
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DOI：
10.1109/tgrs.2015.2503885
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发表时间：
2016-05-01
影响因子：
8.2
通讯作者：
Bruzzone, Lorenzo
中科院分区：
文献类型：
--
作者：
Persello, Claudio;Bruzzone, Lorenzo
提出了一种基于核的高光谱图像分类特征选择方法。该方法的目标是选择原始特征的子集：1)与所考虑的分类问题相关(判别式)，即保持输入和输出变量之间的函数关系；2)跨不同领域不变(稳定)，即最小化源域和目标域之间的数据集偏移。域可以与在不同地理区域或同一区域不同时间收集的高光谱图像相关联。我们提出了一种新的数据集移动度量来评估区域稳定性，该度量计算再生核Hilbert空间中源区域和目标区域之间的条件分布的距离。这种度量是在条件分布的核嵌入的基础上定义的，从而产生了不需要估计类别分布的非参数方法。所采用的搜索策略基于一种多目标优化算法，该算法对Pareto最优解估计的准则函数中的两项进行优化。这导致了在迁移学习环境中执行特征选择的有效方法。在两幅高光谱图像上的实验结果表明，该方法能有效地选择泛化能力较强的特征。
This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities.