Hyperspectral remote sensing image destriping via spectral-spatial factorization

Hyperspectral images (HSIs) are gradually playing an important role in many fields because of their ability to obtain spectral information. However, sensor response differences and other reasons may lead to the generation of stripe noise in HSIs, which will greatly degrade the image quality. To solve the problem of HSIs destriping, a new iterative method via spectral-spatial factorization is proposed. We first rearrange the HSI data to get a new two-dimensional matrix. Then the original noise-free HSI is decomposed into a spectral information matrix and a spatial information matrix. The sparsity of stripe noise, the group sparsity of spatial information matrix, the smoothness of spectral information matrix can be used to achieve sufficient removal of stripe noise while effectively retaining spectral information and spatial details of the original HSI. Numerical tests on simulated datasets show that our method achieves an average PSNR growth above 4dB and a better SSIM result. The proposed method also obtains good results when processing real datasets polluted by Gaussian noise and stripe noise.