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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction
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Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction

机译：高光谱图像特征提取的局部补丁判别度量学习

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摘要

In hyperspectral image (HSI) classification, feature extraction is one important step. Traditional methods, e.g., principal component analysis (PCA) and locality preserving projection, usually neglect the information of within-class similarity and between-class dissimilarity, which is helpful to the improvement of classification. On the other hand, most of these methods, e.g., PCA and linear discriminative analysis, consider that the HSI data lie on a low-dimensional manifold or each class is on a submanifold. However, some class data of HSI may lie on a multimanifold. To avoid these problems, we propose a method for feature extraction in HSIs, assuming that a local region resides on a submainfold. In our method, we deal with the data region by region by taking into account the different discriminative locality information. Then, under the metric learning framework, a robust distance metric is learned. It aims to learn a subspace in which the samples in the same class are as near as possible while the samples in different classes are as far as possible. Encouraging experimental results on two available hyperspectral data sets indicate that our proposed algorithm outperforms many existing feature extract methods for HSI classification.
机译：在高光谱图像（HSI）分类中，特征提取是重要的一步。传统方法，例如主成分分析（PCA）和位置保留投影，通常会忽略类内相似性和类间不相似性的信息，这有助于改进分类。另一方面，大多数这些方法，例如PCA和线性判别分析，都认为HSI数据位于低维流形上，或者每个类都位于子流形上。但是，HSI的某些分类数据可能位于多歧管上。为避免这些问题，我们提出了一种HSI中特征提取的方法，假设本地区域位于子主体上。在我们的方法中，我们通过考虑不同的区分性局部信息来逐区域处理数据。然后，在度量学习框架下，学习鲁棒的距离度量。它旨在学习一个子空间，其中相同类别的样本尽可能地近，而不同类别的样本则尽可能远。在两个可用的高光谱数据集上的令人鼓舞的实验结果表明，我们提出的算法优于许多现有的HSI分类特征提取方法。

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