3D-attention and residual dense-based generative adversarial network for cloud removal

Cong Ma; Zhongyu Chen; Shukai Duan

doi:10.1117/12.2674811

2 May 2023 3D-attention and residual dense-based generative adversarial network for cloud removal

Cong Ma, Zhongyu Chen, Shukai Duan

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421W (2023) https://doi.org/10.1117/12.2674811
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Remote sensing (RS) technology plays an increasingly dominant role in Earth observation. However, cloud contamination is a serious hinder in analysis of RS images. Aiming at the problems that the present cloud removal methods remain cloud residues and lose ground scene details in the restored image, We propose a 3D-attention and residual dense based generative adversarial network (3DA-RDGAN) to remove the cloud. We first introduce the residual dense block (RDB) into the generator, so it learns plentiful local characteristics via dense connection and integrats different levels of features via residual learning to restore ground object information. secondly, a 3D attention module (3DAM) is inserted to each RDB to infer the 3-D attention weights for the feature maps without adding parameters of the original network. Under the guidance of attention loss, 3DAM effectively helps the network pay more attention to the cloud areas and discover the difference between cloud and ground scenes. The proposed 3DA-RDGAN is tested on the open source RICE dataset, and its effect is compared with several other existing deep learning methods. The results indicate the superiority of 3DA-RDGAN in cloud removal for RS images.

Citation Download Citation

Cong Ma, Zhongyu Chen, and Shukai Duan "3D-attention and residual dense-based generative adversarial network for cloud removal", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421W (2 May 2023); https://doi.org/10.1117/12.2674811

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KEYWORDS

Clouds

Remote sensing

Convolution

Deep learning

Education and training

Neural networks

Image analysis

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