Hyperspectral images (HSIs) have fine spectral information, and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features, and eliminate redundant features from hyperspectral data are the main research focus of this article. Here, we propose a spectral-spatial feature extraction method based on ensemble empirical mode decomposition for HSI classification, which contains several steps as follows: First, the dimension reduction for HSI is performed by using the principal component analysis method. Second, in order to decrease the sensitivity to noise, and extract rough outline features, the adaptive total variation filtering (ATVF) is conducted on the selected principal components. Furthermore, by using the ensemble empirical mode decomposition (EEMD) to resolve each spectral band into sequence components, the features of HSIs can be better coalesced into the transform domain. Finally, the first $K$ principal components of the input image, and the outputs of the ATVF, and EEMD are integrated into a stacking system to obtain the final feature image, which is then classified by a pixel-wise classifier. The experimental results of three authentic hyperspectral datasets show that the proposed algorithm obtains a superior classification performance compared with other methods.

中文翻译：

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用于高光谱图像分类的基于集成 EMD 的光谱空间特征提取

高光谱图像（HSI）具有精细的光谱信息和丰富的空间信息，其中特征质量是影响分类性能的关键因素之一。因此，如何从高光谱数据中提取本质特征，去除冗余特征是本文的主要研究重点。在这里，我们提出了一种基于集成经验模态分解的HSI分类谱空间特征提取方法，包括以下几个步骤：首先，使用主成分分析方法对HSI进行降维。其次，为了降低对噪声的敏感性，提取粗略的轮廓特征，对选定的主成分进行自适应全变差滤波（ATVF）。此外，通过使用集成经验模式分解 (EEMD) 将每个谱带分解为序列分量，HSI 的特征可以更好地合并到变换域中。最后，输入图像的第一个 $K$ 主成分，以及 ATVF 和 EEMD 的输出被集成到一个堆叠系统中以获得最终的特征图像，然后由像素级分类器进行分类。三个真实的高光谱数据集的实验结果表明，与其他方法相比，该算法获得了优越的分类性能。和 EEMD 被集成到一个堆叠系统中以获得最终的特征图像，然后由像素级分类器进行分类。三个真实的高光谱数据集的实验结果表明，与其他方法相比，该算法获得了优越的分类性能。和 EEMD 被集成到一个堆叠系统中以获得最终的特征图像，然后由像素级分类器进行分类。三个真实的高光谱数据集的实验结果表明，与其他方法相比，该算法获得了优越的分类性能。