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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Supervised Feature Extraction of Hyperspectral Images Using Partitioned Maximum Margin Criterion
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Supervised Feature Extraction of Hyperspectral Images Using Partitioned Maximum Margin Criterion

机译：使用分区最大余量准则的高光谱图像监督特征提取

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摘要

Dimensionality reduction is an important task where the aim is to reduce the number of features and make the system less time consuming for classification. Here, the drawbacks of Fisher's linear-discriminant-analysis-based feature extraction (FE) methods are addressed and a proposal is made to overcome it as well as to reduce the Hughes phenomenon and computational complexity of the system. The proposed FE technique initially partitions the complete set of features into several highly correlated subgroups. Then a linear transformation is performed using a maximal margin criterion over each subgroup. The proposed method is supervised in nature, because prior information about the class label of data is required to calculate the maximum margin criterion based on interclass and intraclass scatter matrices. Experiments are conducted with the PaviaU and Indian pine data sets, and the results are compared with five state-of-the-art techniques, both qualitatively and quantitatively, to demonstrate the effectiveness of the proposed method.
机译：降维是一项重要任务，其目的是减少特征数量并减少系统分类的时间。在此，解决了基于费舍尔基于线性判别分析的特征提取（FE）方法的缺点，并提出了克服它的建议，并减少了休斯现象和系统的计算复杂性。所提出的有限元技术最初将整个特征集划分为几个高度相关的子组。然后，使用每个子组上的最大余量准则执行线性变换。由于需要使用有关数据类别标签的先验信息来计算基于类别间和类别内散布矩阵的最大余量准则，因此该方法在本质上受到监督。使用PaviaU和印度松数据集进行了实验，并将结果与​​定性和定量的五种最新技术进行了比较，以证明该方法的有效性。

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