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.
Abstract (translated)
高光谱图像（HSI）去噪是一种关键的预处理程序，用于改善后续HSI解释和应用的性能。在本文中，通过组合空间谱深度卷积神经网络（HSID-CNN）学习噪声和干净HSI之间的非线性端到端映射，提出了一种基于深度学习的新方法。 。空间和频谱信息都同时分配给建议的网络。此外，分别采用多尺度特征提取和多级特征表示来捕获多尺度空间光谱特征，并融合不同级别的特征表示以进行最终恢复。仿真和实际数据实验表明，所提出的HSID-CNN在定量评价指标，视觉效果和HSI分类准确性方面均优于许多主流方法。
URL
https://arxiv.org/abs/1806.00183
