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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples
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Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples

机译：基于波段聚类的特征提取有限训练样本对高光谱图像进行分类

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摘要

Feature extraction plays a central role in classification of hyperspectral data. We propose a clustering-based feature extraction (CBFE) method in this letter. The proposed method is supervised and only needs to calculate the first-order statistics. Thus, CBFE has better performance than some popular supervised feature extraction methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted feature extraction in small sample size situation. In addition, CBFE works better than unsupervised approaches such as principal component analysis in classification applications. CBFE considers a vector associated with each band that is composed by the mean values of all classes in that band. Then, a clustering method such as $k$-means is run to group the similar bands in one cluster. The selected number of clusters is equal to the number of extracted features. Experiments carried out on two different hyperspectral data sets demonstrate that the CBFE has better performance in comparison with some conventional feature extraction methods.
机译：特征提取在高光谱数据分类中起着核心作用。在本文中，我们提出了一种基于聚类的特征提取（CBFE）方法。该方法在监督下，只需要计算一阶统计量即可。因此，在小样本量情况下，CBFE的性能要优于一些流行的监督特征提取方法，例如线性判别分析，广义判别分析和非参数加权特征提取。另外，CBFE比分类方法中的主成分分析等无监督方法更好。 CBFE考虑与每个频带关联的向量，该向量由该频带中所有类别的平均值组成。然后，运行诸如$ k $ -means之类的聚类方法将相似的波段分组到一个聚类中。所选的簇数等于提取的特征数。在两个不同的高光谱数据集上进行的实验表明，与某些常规特征提取方法相比，CBFE具有更好的性能。

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