ADD-UNet: An Adjacent Dual-Decoder UNet for SAR-to-Optical Translation

Author:

Luo Qingli1ORCID,Li Hong1,Chen Zhiyuan1,Li Jian1

Affiliation:

1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China

Abstract

Synthetic aperture radar (SAR) imagery has the advantages of all-day and all-weather observation. However, due to the imaging mechanism of microwaves, it is difficult for nonexperts to interpret SAR images. Transferring SAR imagery into optical imagery can better improve the interpretation of SAR data and support the further fusion research of multi-source remote sensing. Methods based on generative adversarial networks (GAN) have been proven to be effective in SAR-to-optical translation tasks. To further improve the translation results of SAR data, we propose a method of an adjacent dual-decoder UNet (ADD-UNet) based on conditional GAN (cGAN) for SAR-to-optical translation. The proposed network architecture adds an adjacent scale of the decoder to the UNet, and the multi-scale feature aggregation of the two decoders improves structures, details, and edge sharpness of generated images while introducing fewer parameters compared with UNet++. In addition, we combine multi-scale structure similarity (MS-SSIM) loss and L1 loss as loss functions with cGAN loss together to help preserve structures and details. The experimental results demonstrate the superiority of our method compared with several state-of-the-art methods.

Funder

Key Project of Tianjin Natural Science Foundation

National Engineering Laboratory for Digital Construction and Evaluation Technology of Urban Rail Transit

Tianjin Transportation Science and Technology Development Project

National Natural Science Foundation of China Grant

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MSF: A Multi-Scale Fusion Generative Adversarial Network for SAR-to-Optical Image Translation;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Deep Reverse Attack on SIFT Features With a Coarse-to-Fine GAN Model;IEEE Transactions on Circuits and Systems for Video Technology;2024-07

3. Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN;International Journal of Computational Intelligence Systems;2024-06-10

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