In real-world applications, the hyperspectral image (HSI) classification faces two critical problems: one is the Hughes phenomenon and the other is the large spatial and spectral variabilities among the pixels of the HSI, especially for the case where only few labeled pixels per class are available. To address these problems, we propose a united framework for the hybrid representation model based on two dictionaries in this paper, where the input pixel (or its feature) is explicitly decomposed into the class-specific component and the variation component via the Bayesian approach. Based on the proposed framework, two hybrid representation-based classification approaches, named as the sparse and dense hybrid representation-based classification and the hybrid sparse representation-based classification, are proposed based on different priors on the separate components over the associated dictionaries. Moreover, the techniques for designing the associated dictionaries and estimating the hyperparameters are also present so the practicality of the proposed approaches is further improved. Experimental results show that the proposed two approaches outperform the conventional classifiers and are robust to the spatial and spectral variabilities, based on four real hyperspectral datasets.

中文翻译：

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基于字典的混合表示用于高光谱图像分类

在实际应用中，高光谱图像 (HSI) 分类面临两个关键问题：一个是休斯现象，另一个是 HSI 像素之间的空间和光谱变异性大，特别是对于每个标记像素很少的情况。可以上课。为了解决这些问题，我们在本文中提出了一个基于两个字典的混合表示模型的统一框架，其中输入像素（或其特征）通过贝叶斯方法显式分解为特定于类的分量和变化分量。基于所提出的框架，两种基于混合表示的分类方法，称为基于稀疏和密集混合表示的分类和基于混合稀疏表示的分类，是基于对相关字典上的单独组件的不同先验提出的。此外，还存在用于设计关联字典和估计超参数的技术，因此进一步提高了所提出方法的实用性。实验结果表明，基于四个真实高光谱数据集，所提出的两种方法优于传统分类器，并且对空间和光谱变化具有鲁棒性。