Abstract Hyperspectralimages (HSIs) can provide powerful spectral discriminative information for the land-covers, thus is widely used in classification and target detection. However, HSIs always suffer from the curse of high dimensionality due the high spectral dimension, therefore dimension reduction and feature extraction are essential for the application of HSIs. In this paper, we propose an unsupervised feature extraction method for HSIs using combined low rank representation and locally linear embedding (LRR and LLE). LRR can structurally represent the intrinsic property of union of low-rank subspaces and LLE can employ the spatial correlation information. Two real HSI datasets are used in the experiments and the classification results using support vector machine (SVM) demonstrate that the features extracted by LRR LLE are more discriminative than the state-of-art methods. The classification accuracy of LRR LLE versus IR improved by an average of 4.47% and 2.97% on OA and AA, respectively; compared with the original data, it increased by approximately 12.07% and 7.35%.

中文翻译：

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一种基于低秩表示和局部线性嵌入的高效高光谱图像降维方法

摘要 高光谱图像（HSI）可以为地表覆盖提供强大的光谱判别信息，因此被广泛应用于分类和目标检测。然而，由于高光谱维数，HSI 总是遭受高维的诅咒，因此降维和特征提取对于 HSI 的应用至关重要。在本文中，我们提出了一种使用组合低秩表示和局部线性嵌入（LRR 和 LLE）的 HSI 无监督特征提取方法。LRR 可以在结构上表示低秩子空间并集的内在属性，而 LLE 可以利用空间相关信息。实验中使用了两个真实的 HSI 数据集，使用支持向量机 (SVM) 的分类结果表明，LRR LLE 提取的特征比最先进的方法更具辨别力。LRR LLE 与 IR 的分类准确率在 OA 和 AA 上分别平均提高了 4.47% 和 2.97%；与原始数据相比，分别增加了约12.07%和7.35%。