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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images
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Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images

机译：基于加权稀疏图的高光谱图像降维

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摘要

Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores the sparsity of the HSI data and can achieve good results. However, in most cases, locality is more important than sparsity when learning the features of the data. In this letter, we propose an extended SGE method: the weighted sparse graph based DR (WSGDR) method for HSIs. WSGDR explicitly encourages the sparse coding to be local and pays more attention to those training pixels that are more similar to the test pixel in representing the test pixel. Furthermore, WSGDR can offer data-adaptive neighborhoods, which results in the proposed method being more robust to noise. The proposed method was tested on two widely used HSI data sets, and the results suggest that WSGDR obtains sparser representation results. Furthermore, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.
机译：降维（DR）是高光谱图像（HSI）分类的重要且有用的预处理步骤。近年来，稀疏图嵌入（SGE）已被广泛应用于HSI的DR中。 SGE探索了HSI数据的稀疏性并可以取得良好的结果。但是，在大多数情况下，学习数据的特征时，局部性比稀疏性更为重要。在这封信中，我们提出了一种扩展的SGE方法：针对HSI的基于加权稀疏图的DR（WSGDR）方法。 WSGDR明确鼓励稀疏编码是局部的，并在表示测试像素时更多地关注那些与测试像素更相似的训练像素。此外，WSGDR可以提供数据自适应邻域，这导致所提出的方法对噪声更鲁棒。该方法在两个广泛使用的HSI数据集上进行了测试，结果表明WSGDR获得了稀疏表示结果。此外，实验结果还证实了所提出的WSGDR方法优于其他最新DR方法的优越性。

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