Abstract. Graph embedding (GE) frameworks are used for extracting the discriminative features of hyperspectral images (HSIs). However, it is difficult to select a proper neighborhood size for graph construction. To overcome this difficulty, a semisupervised feature extraction (FE) method, called semisupervised collaborative representation graph embedding (SCRGE), is proposed. The proposed algorithm utilizes collaborative representation (CR) to obtain the collaborative coefficients of labeled and unlabeled samples. Then, a semisupervised graph is constructed using the collaborative coefficients of the labeled samples within the same class and the collaborative coefficients of the unlabeled samples, and an interclass graph is constructed using the collaborative coefficients of the labeled samples in different classes. Finally, a projection matrix for FE is obtained by embedding these graphs into a low-dimensional space. SCRGE not only inherits the merits of CR to reveal the collaborative reconstructive properties of data but also enhances intraclass compactness and interclass separability to improve the discriminating power for classification. Experimental results on three real HSIs datasets demonstrate that SCRGE outperforms other state-of-the-art FE methods in terms of classification accuracy.

中文翻译：

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用于高光谱图像的半监督协作表示图嵌入

摘要。图嵌入 (GE) 框架用于提取高光谱图像 (HSI) 的判别特征。然而，很难为图构建选择合适的邻域大小。为了克服这个困难，提出了一种称为半监督协作表示图嵌入（SCRGE）的半监督特征提取（FE）方法。所提出的算法利用协同表示（CR）来获得标记和未标记样本的协同系数。然后，利用同一类标记样本的协同系数和未标记样本的协同系数构建半监督图，利用不同类标记样本的协同系数构建类间图。最后，FE 的投影矩阵是通过将这些图嵌入到低维空间中获得的。SCRGE 不仅继承了 CR 的优点来揭示数据的协同重构特性，而且还增强了类内紧凑性和类间可分离性，以提高分类的判别力。在三个真实 HSI 数据集上的实验结果表明，SCRGE 在分类精度方面优于其他最先进的 FE 方法。