Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral–spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI. The extraction of local (instead of global) features, particularly in HSI, can be more effective for characterizing the objects within an image. In SpaSSA, conventional SSA and 2-D SSA are combined and adaptively applied to each superpixel derived from an oversegmented HSI. According to the size of the derived superpixels, either SSA or 2-D singular spectrum analysis (2D-SSA) is adaptively applied for feature extraction, where the embedding window in 2D-SSA is also adaptive to the size of the superpixel. Experimental results on the three datasets have shown that the proposed SpaSSA outperforms both SSA and 2D-SSA in terms of classification accuracy and computational complexity. By combining SpaSSA with the principal component analysis (SpaSSA-PCA), the accuracy of land-cover analysis can be further improved, outperforming several state-of-the-art approaches.

中文翻译：

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SpaSSA：用于高光谱图像中无监督空间光谱特征提取的超像素自适应 SSA


奇异光谱分析（SSA）最近已成功应用于高光谱图像（HSI）的特征提取，包括谱域中的传统（一维）SSA和空间域中的二维SSA。然而，它也存在一些缺点，如对窗口大小敏感、大窗口下计算复杂度高、无法提取联合谱空间特征等。为了解决这些问题，在本文中，我们提出了超像素自适应 SSA（SpaSSA），即用于利用 HSI 局部空间信息的超像素自适应 SSA。提取局部（而不是全局）特征，特别是在 HSI 中，可以更有效地表征图像中的对象。在 SpaSSA 中，传统的 SSA 和 2-D SSA 被组合并自适应地应用于从过分割的 HSI 导出的每个超像素。根据导出的超像素的大小，自适应地应用SSA或二维奇异谱分析（2D-SSA）进行特征提取，其中2D-SSA中的嵌入窗口也自适应于超像素的大小。三个数据集上的实验结果表明，所提出的 SpaSSA 在分类精度和计算复杂度方面优于 SSA 和 2D-​​SSA。通过将 SpaSSA 与主成分分析 (SpaSSA-PCA) 相结合，可以进一步提高土地覆盖分析的准确性，优于几种最先进的方法。