HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network
HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network
复制标题
HSI-DeNet：通过卷积神经网络恢复高光谱图像
DOI：
10.1109/tgrs.2018.2859203
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发表时间：
2019-02-01
影响因子：
8.2
通讯作者：
Liao, Wenshan
中科院分区：
文献类型：
--
作者：
Chang, Yi;Yan, Luxin;Liao, Wenshan
高光谱图像中的光谱信息和空间信息是一枚硬币的两面。如何对它们进行联合建模是HSI去噪的关键问题，包括随机噪声、结构条纹噪声和死像素/线条。在本文中，我们引入了深度卷积神经网络(CNN)来实现这一目标。学习后的滤波器能够很好地提取其局部感受场内的空间信息。同时，谱相关可以通过学习的2-D滤波器的多个通道，即每层中的滤波器数目来描述。因此，我们的基于CNN的HSI去噪方法(HSI-Denet)比以前的方法有三个优点。首先，HSI-Denet是一种基于张量的方法，在不破坏谱空间结构的前提下，直接学习每一层的滤波器。其次，HSI-Denet可以同时适应HSI中的各种噪声。此外，通过稍微修改第一层和最后一层中的滤光片的通道，我们的方法对于单幅图像和多幅图像都是灵活的。最后但同样重要的是，我们的方法在测试阶段非常快，这使得它在实际应用中更加实用。所提出的HSI-DENet在几个HSI上进行了广泛的评估，并且在速度和性能方面都优于最先进的HSI-DENet。
The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of the same coin. How to jointly model them is the key issue for HSIs' noise removal, including random noise, structural stripe noise, and dead pixels/lines. In this paper, we introduce the deep convolutional neural network (CNN) to achieve this goal. The learned filters can well extract the spatial information within their local receptive filed. Meanwhile, the spectral correlation can be depicted by the multiple channels of the learned 2-D filters, namely, the number of filters in each layer. The consequent advantages of our CNN-based HSI denoising method (HSI-DeNet) over previous methods are threefold. First, the proposed HSI-DeNet can be regarded as a tensor-based method by directly learning the filters in each layer without damaging the spectral-spatial structures. Second, the HSI-DeNet can simultaneously accommodate various kinds of noise in HSIs. Moreover, our method is flexible for both single image and multiple images by slightly modifying the channels of the filters in the first and last layers. Last but not least, our method is extremely fast in the testing phase, which makes it more practical for real application. The proposed HSI-DeNet is extensively evaluated on several HSIs, and outperforms the state-of-the-art HSI-DeNets in terms of both speed and performance.