Abstract Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed. First, the noisy endmember spectra are extracted by using the vertex component analysis algorithm. Then the singular spectrum analysis is used to denoise the endmember spectrum. When compared with the hyperspectral data as a whole, the amounts of endmember spectral data are known to be small. If only denoising endmember spectral data were to be performed, then the denoising time will be greatly improved, and image information can be effectively preserved. The method has high precision and fast speed for unmixing the noisy hyperspectral image. The advantages of this method will be more apparent when dealing with large amounts of hyperspectral data. In this article, different noise images are experimented with using this method, and strong experimental results are obtained.

中文翻译：

---

基于顶点分量分析和奇异谱分析算法的噪声高光谱图像快速解混

摘要 高效去噪对于高光谱图像的解混具有重要意义。在本研究中，提出了一种基于顶点分量分析和奇异谱分析相结合的噪声高光谱图像快速解混方法。首先，使用顶点分量分析算法提取噪声端元谱。然后使用奇异谱分析对端元谱进行去噪。与整个高光谱数据相比，已知端元光谱数据的数量很小。如果只对端元光谱数据进行去噪，则去噪时间将大大提高，可以有效地保留图像信息。该方法对噪声高光谱图像的解混精度高、速度快。在处理大量高光谱数据时，这种方法的优势会更加明显。在本文中，使用该方法对不同的噪声图像进行了实验，得到了很强的实验结果。