Paper Info
Title
RSDehazeNet: Dehazing Network With Channel Refinement for Multispectral Remote Sensing Images
Abstract
Multispectral remote sensing (RS) images are often contaminated by the haze that degrades the quality of RS data and reduces the accuracy of interpretation and classification. Recently, the emerging deep convolutional neural networks (CNNs) provide us new approaches for RS image dehazing. Unfortunately, the power of CNNs is limited by the lack of sufficient hazy-clean pairs of RS imagery, which makes supervised learning impractical. To meet the data hunger of supervised CNNs, we propose a novel haze synthesis method to generate realistic hazy multispectral images by modeling the wavelength-dependent and spatial-varying characteristics of haze in RS images. The proposed haze synthesis method not only alleviates the lack of realistic training pairs in multispectral RS image dehazing but also provides a benchmark data set for quantitative evaluation. Furthermore, we propose an end-to-end RSDehazeNet for haze removal. We utilize both local and global residual learning strategies in RSDehazeNet for fast convergence with superior performance. Channel attention modules are incorporated to exploit strong channel correlation in multispectral RS images. Experimental results show that the proposed network outperforms the state-of-the-art methods for synthetic data and real Landsat-8 OLI multispectral RS images.
Year
DOI
Venue
2021
10.1109/TGRS.2020.3004556
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Convolutional neural network (CNN),haze data synthesis,haze removal,Landsat-8 OLI multispectral image,wavelength dependence
Journal
59
Issue
ISSN
Citations 
3
0196-2892
4
PageRank 
References 
Authors
0.43
26
6
Name
Order
Citations
PageRank
Jianhua Guo143.13
Jingyu Yang227431.04
Huanjing Yue3246.89
Hai Tan441.44
Chunping Hou550151.32
Kun Li693.33