Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
复制标题
通过最小化奇异值部分和和超像素分割进行高光谱图像去噪
DOI：
10.1016/j.neucom.2018.11.039
复制
发表时间：
2019-02-22
期刊：
影响因子：
6
通讯作者：
Cui, Rongmei
中科院分区：
文献类型：
--
作者：
Liu, Yang;Shan, Caifeng;Cui, Rongmei
高光谱图像在获取过程中经常受到噪声的干扰，从而显著降低了高光谱图像的分辨能力。因此，HSI去噪成为应用前必不可少的预处理步骤。本文提出了一种结合部分奇异值和（Partial Sum of Singular Values，PSSV）和超像素分割的HSI去噪方法SS-PSSV，能够有效去除噪声。基于同一信号的不同频带之间存在高相关性的事实，很容易知道不同频带之间的低秩特性。为此，PSSV被利用，并且为了更好地挖掘像素的低秩属性，我们引入了超像素分割方法，该方法允许HSI中具有高相似性的像素尽可能多地被分组在同一子块中。大量的实验表明，该算法优于国家的最先进的。（C）2018爱思唯尔B. V.版权所有。
Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI's discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art. (C) 2018 Elsevier B.V. All rights reserved.