.png){kind=icon}
Return
Remote sensing image cloud removal based on multi-scale spatial information perception
.png){kind=icon}
PRE
AI
DOI：10.1007/s00530-024-01442-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Remote sensing imagery is indispensable in diverse domains, including geographic information systems, climate monitoring, agricultural planning, and disaster management. Nonetheless, cloud cover can drastically degrade the utility and quality of these images. Current deep learning-based cloud removal methods rely on convolutional neural networks to extract features at the same scale, which can overlook detailed and global information, resulting in suboptimal cloud removal performance. To overcome these challenges, we develop a method for cloud removal that leverages multi-scale spatial information perception. Our technique employs convolution kernels of various sizes, enabling the integration of both global semantic information and local detail information. An attention mechanism enhances this process by targeting key areas within the images, and dynamically adjusting channel weights to improve feature reconstruction. We compared our method with current popular cloud removal methods across three datasets, and the results show that our proposed method improves metrics such as PSNR, SSIM, and cosine similarity, verifying the effectiveness of our method in cloud removal.
Keywords:
Cloud removal
Multi-scale feature extraction
Attention mechanism
Journal
IF:
3.1
Papers:
2.8K
Citations:
2.7K
Organization
Cited Papers
ShareSaveShareSave
A multi-scale feature fusion spatial-channel attention model for background subtraction.png)
MULTIMEDIA SYSTEMS
MULTIMEDIA SYSTEMSIF3.1
PREAI
ShareSaveThermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized personsIndoor Air
OAAI
Indoor AirIF0
OAAIShareSave
View more