激光与光电子学进展, 2020, 57 (12): 121102, 网络出版: 2020-06-03  

结合地物类别和低秩特性的高光谱图像降噪 下载: 983次

Hyperspectral Image Denoising By Combining Ground Object Features with Low-Rank Characteristics
作者单位
1 上海海洋大学信息学院, 上海 201306
2 上海电力大学, 上海 200090
摘要
针对现有方法不易确定划分高光谱图像子块的大小和个数,仅考虑子块内低秩性等不足,提出一种结合地物类别和低秩特性的高光谱图像降噪方法。根据地物数据先验知识的类别数,简单划分子块的个数,指定最优参数明确分块大小,再通过相同地物中像素空间和光谱的相关性定义同物空谱低秩特性,最后结合整幅高光谱图像的光谱低秩特性,并根据低秩矩阵恢复模型求解降噪图像。在Washington DC Mall和Indian Pines数据集上进行实验,结果表明:所提方法不仅对每一类地物噪声的降噪效果有所提高,而且针对更为严重的随机噪声和稀疏噪声的混合噪声,也能够达到更好的降噪效果。
Abstract
It is difficult to determine the size and number of sub-blocks in a hyperspectral image using the existing methods because of the low rank of the sub-blocks and other associated disadvantages. Therefore, we propose a hyperspectral image denoising method, which combines the features of the ground objects with the low-rank characteristics. Further, the number of sub-blocks are divided with respect to the number of categories of prior knowledge of ground object data, and optimal parameters are specified for determining the size of the blocks. Then, the low-rank characteristics of the same object space spectrum are obtained based on the correlation of the pixel space and spectrum with respect to the same feature. Finally, the spectral low-rank characteristics of the entire hyperspectral image are combined, and the noise-reduced image is obtained according to the low-rank matrix recovery model. Experiments conducted on the Washington DC Mall and Indian Pines datasets demonstrate that the proposed method not only improves the noise reduction effect with respect to each type of ground noise but also targets mixed noise containing more severe random noise and sparse noise.

黄冬梅, 李永兰, 张明华, 宋巍. 结合地物类别和低秩特性的高光谱图像降噪[J]. 激光与光电子学进展, 2020, 57(12): 121102. Dongmei Huang, Yonglan Li, Minghua Zhang, Wei Song. Hyperspectral Image Denoising By Combining Ground Object Features with Low-Rank Characteristics[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121102.

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