Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.

中文翻译：

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高光谱图像的高效光谱特征提取框架

从高光谱图像中提取各种光谱特征已成为近年来的热门话题。但是，这些模型在训练和测试上很耗时，并且判别能力差，导致分类精度低。在本文中，我们为高光谱数据的光谱设计了有效的特征提取框架。我们构造了一个结构化的字典来对光谱信息进行编码，并应用学习机来映射编码系数。为了减少训练和测试时间，将稀疏约束替换为块对角约束以加快迭代速度，并使用高效的极限学习机来拟合光谱特征。为了优化模型的判别能力，我们首先添加光谱卷积以提取大量光谱信息。然后，我们设计了词典的共享约束，使词典的共同特征得到更有效的表达，从而提高词典的判别和重构能力。在各种数据库上的实验结果表明，与深度学习模型相比，所提出的特征提取框架不仅可以大大减少训练和测试时间，而且还具有非常具有竞争力的准确性。