Hierarchical Semantic-Guided Contextual Structure-Aware Network for Spectral Satellite Image Dehazing

Author:

Yang Lei12,Cao Jianzhong12,Wang Hua12ORCID,Dong Sen1,Ning Hailong3ORCID

Affiliation:

1. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Abstract

Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

Funder

Scientific Research Program Funded by Shaanxi Provincial Education Department

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Publisher

MDPI AG

Reference44 articles.

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