Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network

By Qiangqiang Yuan, Qiang Zhang, Jie Li, Huanfeng Shen, Liangpei Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2018

449 citations recorded by the source corpus.

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a nonlinear end-to-end mapping between the noisy and clean HSIs with a combined spatial–spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multiscale feature extraction and multilevel feature representation are, respectively, employed to capture both the multiscale spatial–spectral feature and fuse different feature representations for the final restoration. The simulated and real-data experiments demonstrate that the proposed HSID-CNN outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f675035662882dc44b2a557104dcda24e783a281

Citations in the corpus

• Deep Learning on Image Denoising: An overview (2019, unknown): “For example, Yuan et al. [249] combined a deep CNN, residual learning and multiscale knowledge to remove the noise from hyperspectral-noisy images.”