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arXiv · 1806.00183

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

Qiangqiang Yuan·Qiang Zhang·Jie Li·Huanfeng Shen·Liangpei Zhang
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Abstract

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 non-linear 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, multi-scale feature extraction and multi-level feature representation are respectively employed to capture both the multi-scale spatial-spectral feature and fuse the feature representations with different levels 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.

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Qiangqiang Yuan, Qiang Zhang, Jie Li, Huanfeng Shen, Liangpei Zhang. 2018-08-11. Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network. https://doi.org/10.1109/tgrs.2018.2865197

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