An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing

Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It fi...

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Vydané v:IEEE sensors journal Ročník 22; číslo 11; s. 10890 - 10900
Hlavní autori: Zhao, Liquan, Zhang, Yupeng, Cui, Ying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Atmospheric modeling
Coders
Distillation
encoder-decoder
Encoders-Decoders
Feature extraction
generative adversarial network
Generative adversarial networks
Haze
Learning systems
Modules
multi-scale attention module
Remote sensing
Remote sensing image dehazing
Scattering
Task complexity
Training
ISSN:1530-437X, 1558-1748
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Shrnutí:Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3172132