Denoising Hyperspectral Imagery Using Principal Component Analysis and Block-Matching 4D Filtering

作者：Chen Guangyi^*; Bui Tien D; Quach Kha Gia; Qian Shen En

来源：Canadian Journal of Remote Sensing, 2014, 40(1): 60-66.

DOI：10.1080/07038992.2014.917582

In this article, we propose a new method for denoising hyperspectral imagery. Hyperspectral imagery normally contains a small amount of noise, which can hardly be seen by human eyes thanks to its relatively high signal-to-noise ratio. However, in many remote sensing applications, this amount of noise is still troublesome. In this study, we first perform principal component analysis (PCA) to the hyperspectral data cube to be denoised in order to separate the fine features from the noise in the hyperspectral data cube. Because the first few PCA output channels contain the majority of information in the hyperspectral data cube, we do not denoise these PCA output channel images. We use the block-matching 4D (BM4D) filtering to reduce the noise in the remaining low-energy noisy PCA output channel images. Finally, an inverse PCA transform is performed in order to obtain the denoised hyperspectral data cube. Experimental results show that our proposed method in this work is very competitive when compared with existing methods for hyperspectral imagery denoising.

• 出版日期2014