Reciprocal translation between SAR and optical remote sensing images with cascaded-residual adversarial networks  

作　　者：Shilei FU Feng XU Ya-Qiu JIN 

机构地区：[1]Key Lab for Information Science of Electromagnetic Waves(MoE),Fudan University,Shanghai 200433,China

出　　处：《Science China(Information Sciences)》2021年第2期150-164,共15页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key R&D Program of China(Grant No.2017YFB0502703);Natural Science Foundation of China(Grant Nos.61822107,61571134)。

摘　　要：Despite the advantages of all-weather and all-day high-resolution imaging,synthetic aperture radar(SAR)images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon.However,expert interpreters can be trained by comparing side-byside SAR and optical images to learn the mapping rules from SAR to optical.This paper attempts to develop machine intelligence that is trainable with large-volume co-registered SAR and optical images to translate SAR images to optical version for assisted SAR image interpretation.Reciprocal SAR-optical image translation is a challenging task because it is a raw data translation between two physically very different sensing modalities.Inspired by recent progresses in image translation studies in computer vision,this paper tackles the problem of SAR-optical reciprocal translation with an adversarial network scheme where cascaded residual connections and hybrid L1-GAN loss are employed.It is trained and tested on both spaceborne Gaofen-3(GF-3)and airborne Uninhabited Airborne Vehicle Synthetic Aperture Radar(UAVSAR)images.Results are presented for datasets of different resolutions and polarizations and compared with other state-of-the-art methods.The Frechet inception distance(FID)is used to quantitatively evaluate the translation performance.The possibility of unsupervised learning with unpaired/unregistered SAR and optical images is also explored.Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.

关 键 词：synthetic aperture radar generative adversarial network(GAN) image translation cascaded residual connection Frechet inception distance 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN957.52[自动化与计算机技术—控制科学与工程] TP751[电子电信—信号与信息处理]