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Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image
| Content Provider | MDPI |
|---|---|
| Author | Dou, Hong-Xia Pan, Xiao-Miao Wang, Chao Shen, Hao-Zhen Deng, Liang-Jian |
| Copyright Year | 2022 |
| Description | Hyperspectral images (HSIs) are frequently contaminated by different noises (Gaussian noise, stripe noise, deadline noise, impulse noise) in the acquisition process as a result of the observation environment and imaging system limitations, which makes image information lost and difficult to recover. In this paper, we adopt a 3D-based SSCA block neural network of U-Net architecture for remote sensing HSI denoising, named SSCANet (Spatial and Spectral-Channel Attention Network), which is mainly constructed by a so-called SSCA block. By fully considering the characteristics of spatial-domain and spectral-domain of remote sensing HSIs, the SSCA block consists of a spatial attention (SA) block and a spectral-channel attention (SCA) block, in which the SA block is to extract spatial information and enhance spatial representation ability, as well as the SCA block to explore the band-wise relationship within HSIs for preserving spectral information. Compared to earlier 2D convolution, 3D convolution has a powerful spectrum preservation ability, allowing for improved extraction of HSIs characteristics. Experimental results demonstrate that our method holds better-restored results than other compared approaches, both visually and quantitatively. |
| Starting Page | 3338 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14143338 |
| Journal | Remote Sensing |
| Issue Number | 14 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-11 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Hyperspectral Image Denoising Convolutional Neural Network (cnn) Deep Learning |
| Content Type | Text |
| Resource Type | Article |
