深度学习在光学和SAR影像融合研究进展

成飞飞; 付志涛; 黄亮; 牛宝胜; 陈朋弟; 王雷光; 季欣然

doi:10.11834/jrs.20211136

研究进展 | 浏览量 : 0 下载量: 8602 CSCD: 2

R-PDF
PDF
导出
分享
收藏
专辑

深度学习在光学和SAR影像融合研究进展
Review of deep learning in optical and SAR image fusion
2022年26卷第9期页码：1744-1756
收稿：2021-03-15，

纸质出版：2022-09-07
DOI： 10.11834/jrs.20211136
稿件说明：

移动端阅览

成飞飞，付志涛，黄亮，牛宝胜，陈朋弟，王雷光，季欣然.2022.深度学习在光学和SAR影像融合研究进展.遥感学报，26（9）： 1744-1756 DOI： 10.11834/jrs.20211136.

Cheng F F， Fu Z T， Huang L， Niu B S，Chen P D， Wang L G and Ji X R. 2022. Review of deep learning in optical and SAR image fusion. National Remote Sensing Bulletin， 26（9）：1744-1756 DOI： 10.11834/jrs.20211136.

摘要

遥感影像融合作为影像处理领域中最具有挑战的工作，一直是学术界研究的热点。合成孔径雷达SAR（Synthetic Aperture Radar）具备全天时、全天候、穿透云雾等多种特点，却因存在相干斑噪声等问题，使得影像难以解译。相比之下，光学影像可以反映地物的光谱和空间信息，易于解译，但容易受到云雾干扰，造成信息丢失，将光学与SAR影像数据融合可以实现不同类型传感器成像之间的信息互补，能够更好地为后续的影像分析与解译提供方便。本文首先对光学和SAR影像融合进行了系统性回顾，包括传统融合方法和基于深度学习方法在影像融合方面的最新工作，重点阐述了卷积神经网络CNN（Convolutional Neural Network）、生成式对抗网络GAN（Generative Adversarial Networks）等框架在光学和SAR影像融合中的进展；然后总结了光学和SAR影像融合在深度学习领域开发的数据集，并做了简单介绍和说明；最后，从数据集、时间序列影像融合、融合评价体系和算法轻量化等4个方面对光学和SAR影像融合的未来发展趋势进行了展望。

Abstract

Remote sensing image fusion

as the most challenging work in the field of image processing

has been an academic research hotspot. SAR has various features

such as all-weather service

and penetrating clouds. However

the image is difficult to interpret due to the speckle noise problem. In contrast

optical images can reflect the spectral information of ground objects

which are easy to interpret

but interference by clouds and fog can easily occur

resulting in information loss. The fusion of optical and SAR image data can realize the complementary information between different types of sensor imaging

which can facilitate the subsequent image analysis and interpretation.

In this paper

the research status and future development trend of optical and SAR remote sensing fusion is reviewed. The introduction part presents the motivation of this paper by explaining the importance of image fusion. The second part outlines the classification of optical and SAR image fusion from traditional methods to deep learning methods. The third part presents the datasets in terms of optical and SAR images and explains each dataset in detail. The fourth part summarizes the difficulties and some challenging problems of optical and SAR image fusion and highlights the future trends in the field of optical and SAR image fusion.

The future development trend of fusion has four main aspects

namely

datasets

time series image fusion

fusion evaluation system

and algorithm lightweight. First

having well-targeted and sufficient datasets is an important part of training excellent fusion models

and the production of data sets is the key to improving the applicability of future models. Second

time series optical images are being used as supplementary information to SAR data with the increasing amount of satellite time series data

thus improving the utilization of image fusion. Third

the evaluation of the image fusion performance is an essential topic. No unified evaluation metric is available for objectively and comprehensively evaluating fusion algorithms

and an evaluation metric system in the field of optical and SAR image fusion must be developed. Finally

the lightweight of deep learning algorithms is an important future research direction. This paper provides a reference for researchers in optical and SAR image fusion.

关键词

Keywords

references

Bermudez J D , Happ P N , Feitosa R Q and Oliveira D A B . 2019 . Synthesis of multispectral optical images from SAR/optical multitemporal data using conditional generative adversarial networks . IEEE Geoscience and Remote Sensing Letters , 16 ( 8 ): 1220 - 1224 [ DOI: 10.1109/LGRS.2019.2894734 http://dx.doi.org/10.1109/LGRS.2019.2894734 ]

Chandrakanth R , Saibaba J , Varadan G and Raj P A . 2011 . Feasibility of high resolution SAR and multispectral data fusion // 2011 IEEE International Geoscience and Remote Sensing Symposium . Vancouver, BC : IEEE: 356 - 359 [ DOI: 10.1109/IGARSS.2011.6048972 http://dx.doi.org/10.1109/IGARSS.2011.6048972 ]

Chen Y and Bruzzone L . 2019 . Self-supervised sar-optical data fusion of sentinel-1/-2 images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 1 - 11 [ DOI: 10.1109/TGRS.2021.3128072 http://dx.doi.org/10.1109/TGRS.2021.3128072 ]

Chen K M , Zhou Z X , Xu G L , Fu K and Zhang D B (R & D Personnel) . 2017 . A Multi-source Remote Sensing Image Fusion Method Based on Deep Learning // Chinese Patent , Applicant (patent right) : Institute of Electronics, Chinese Academy of Sciences. Beijing: CN 106295714 A, 2017-01-04

陈克明 , 周志鑫 , 许光銮 , 付琨 , 张道兵（技术研发人员） . 2017 . 一种基于深度学习的多源遥感图像融合方法 // 中国专利，申请(专利权)人：中国科学院电子学研究所. 北京：CN 106295714 A, 2017-01-04

Chen S H , Zhang R F , Su H B , Tian J and Xia J . 2010 . SAR and multispectral image fusion using generalized IHS transform based on à Trous wavelet and EMD decompositions . IEEE Sensors Journal , 10 ( 3 ): 737 - 745 [ DOI: 10.1109/JSEN.2009.2038661 http://dx.doi.org/10.1109/JSEN.2009.2038661 ]

Cheng C Y , Wu X J , Xu T Y and Chen G Y . 2021 . Unifusion: A lightweight unified image fusion network . IEEE Transactions on Instrumentation and Measurement , 70 : 1 - 14 [ DOI: 10.1109/TIM.2021.3109379 http://dx.doi.org/10.1109/TIM.2021.3109379 ]

Chou K C , Golden S A and Willsky A S . 1993 . Multiresolution stochastic models, data fusion, and wavelet transforms . Signal Processing , 34 ( 3 ): 257 - 282 [ DOI: 10.1016/0165-1684(93)90135-W http://dx.doi.org/10.1016/0165-1684(93)90135-W ]

Dupas C A . 2000 . SAR and LANDSAT TM image fusion for land cover classification in the Brazilian Atlantic forest domain . International Archives of Photogrammetry and Remote Sensing , 33 ( 1 ): 96 - 103 .

El-Taweel G S and Helmy A K . 2014 . Fusion of multispectral and full polarimetric SAR images in NSST domain . International Journal of Image Processing , 8 ( 6 ): 497 - 513

Enomoto K , Sakurada K , Wang W M , Kawaguchi N , Matsuoka and Nakamura R . 2018 . Image translation between SAR and optical imagery with generative adversarial nets // 2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia : IEEE: 1752 - 1755 [ DOI: 10.1109/IGARSS.2018.8518719 http://dx.doi.org/10.1109/IGARSS.2018.8518719 ]

Feng P M , Lin Y T , Guan J , Dong Y , He G J , Xia Z H and Shi H F . 2019a . Embranchment CNN based local climate zone classification using Sar and multispectral remote sensing data // 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 6344 - 6347 [ DOI: 10.1109/IGARSS.2019.8898703 http://dx.doi.org/10.1109/IGARSS.2019.8898703 ]

Feng Q L , Yang J Y , Zhu D H , Liu J T , Guo H , Bayartungalag B and Li B G . 2019b . Integrating multitemporal sentinel-1/2 data for coastal land cover classification using a multibranch convolutional neural network: a case of the Yellow River Delta . Remote Sensing , 11 ( 9 ): 1 - 22 [ DOI: 10.3390/rs11091006 http://dx.doi.org/10.3390/rs11091006 ]

Fu S L , Xu F and Jin Y Q . 2021 . Reciprocal translation between SAR and optical remote sensing images with cascaded-residual adversarial networks . Science China Information Sciences , 64 ( 2 ): 1 - 15 [ DOI: 10.1007/s11432-020-3077-5 http://dx.doi.org/10.1007/s11432-020-3077-5 ]

Fuentes Reyes M , Auer S , Merkle N , Henry C and Schmitt M . 2019 . SAR-to-optical image translation based on conditional generative adversarial networks—Optimization, opportunities and limits . Remote Sensing , 11 ( 17 ): 1 - 19 [ DOI: 10.3390/rs11172067 http://dx.doi.org/10.3390/rs11172067 ]

Gao J H , Zhang H and Yuan Q Q . 2019 . Cloud removal with fusion of SAR and optical images by deep learning // Proceedings of the 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) . Shanghai : IEEE: 1 - 3 [ DOI: 10.1109/Multi-Temp.2019.8866939 http://dx.doi.org/10.1109/Multi-Temp.2019.8866939 ]

Ghamisi P , Rasti B , Yokoya N , Wang Q M , Hofle B , Bruzzone L , Bovolo F , Chi M M , Anders K , Gloaguen R , Atkinson P M and Benediktsson J A . 2019 . Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art . IEEE Geoscience and Remote Sensing Magazine , 7 ( 1 ): 6 - 39 [ DOI: 10.1109/MGRS.2018.2890023 http://dx.doi.org/10.1109/MGRS.2018.2890023 ]

Ghassemian H . 2016 . A review of remote sensing image fusion methods . Information Fusion , 32 : 75 - 89 [ DOI: 10.1016/j.inffus.2016.03.003 http://dx.doi.org/10.1016/j.inffus.2016.03.003 ]

Goodfellow I J , Pouget-Abadie J , Mirza M , Xu B , Warde-Farley D , Ozair S , Courville A and Bengio Y . 2014 . Generative adversarial nets // Proceedings of the 27th International Conference on Neural Information Processing Systems . Montreal : MIT Press: 2672 - 2680

Goshtasby A A and Nikolov S . 2007 . Image fusion: advances in the state of the art . Information Fusion , 8 ( 2 ): 114 - 118 [ DOI: 10.1016/j.inffus.2006.04.001 http://dx.doi.org/10.1016/j.inffus.2006.04.001 ]

Grohnfeldt C , Schmitt M and Zhu X X . 2018 . A conditional generative adversarial network to fuse SAR and multispectral optical data for cloud removal from Sentinel-2 images // 2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia : IEEE: 1726 - 1729 [ DOI: 10.1109/IGARSS.2018.8519215 http://dx.doi.org/10.1109/IGARSS.2018.8519215 ]

He W and Yokoya N . 2018 . Multi-temporal Sentinel-1 and -2 data fusion for optical image simulation . ISPRS International Journal of Geo-Information , 7 ( 10 ): 1 - 11 [ DOI: 10.3390/ijgi7100389 http://dx.doi.org/10.3390/ijgi7100389 ]

Hoffmann S , Brust C A , Shadaydeh M and Denzler J . 2019 . Registration of high resolution Sar and optical satellite imagery using fully convolutional networks // 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 5152 - 5155 [ DOI: 10.1109/IGARSS.2019.8898714 http://dx.doi.org/10.1109/IGARSS.2019.8898714 ]

Huang M Y , Xu Y , Qian L X , Shi W L , Zhang Y Q , Bao W , Wang N , Liu X J and Xiang X S . 2021 . The QXS-SAROPT dataset for deep learning in SAR-optical data fusion . arXiv : Image and Video Processing [ DOI: 10.48550/arXiv.2103.08259 http://dx.doi.org/10.48550/arXiv.2103.08259 ]

Huang Y Q , Liao J J , Guo H D and Zhong X L . 2005 . The fusion of multispectral and SAR images based wavelet transformation over urban area // Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium . Seoul : IEEE: 3942 - 3944 [ DOI: 10.1109/IGARSS.2005.1525774 http://dx.doi.org/10.1109/IGARSS.2005.1525774 ]

Ienco D , Interdonato R , Gaetano R and Minh D H T . 2019 . Combining Sentinel-1 and Sentinel-2 satellite image time series for land cover mapping via a multi-source deep learning architecture . ISPRS Journal of Photogrammetry and Remote Sensing , 158 : 11 - 22 [ DOI: 10.1016/j.isprsjprs.2019.09.016 http://dx.doi.org/10.1016/j.isprsjprs.2019.09.016 ]

Isola P , Zhu J Y , Zhou T and Efros A A . 2017 . Image-to-image translation with conditional adversarial networks // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI : IEEE: 5967 - 5976 [ DOI: 10.1109/CVPR.2017.632 http://dx.doi.org/10.1109/CVPR.2017.632 ]

Jia Y H . 1998 . Fusion of Landsat TM and SAR images based on principal component analysis . Remote Sensing Technology and Application , 13 ( 1 ): 46 - 49

贾永红 . 1998 . TM和SAR影像主分量变换融合法 . 遥感技术与应用 , 13 ( 1 ): 46 - 49 [ DOI: 10.3969/j.issn.1004-0323.1998.01.007 http://dx.doi.org/10.3969/j.issn.1004-0323.1998.01.007 ]

Kim S , Song W J and Kim S H . 2018 . Double weight-based SAR and infrared sensor fusion for automatic ground target recognition with deep learning . Remote Sensing , 10 ( 1 ): 1 - 24 [ DOI: 10.3390/rs10010072 http://dx.doi.org/10.3390/rs10010072 ]

Kulkarni S C and Rege P P . 2020 . Pixel level fusion techniques for SAR and optical images: a review . Information Fusion , 59 : 13 - 29 [ DOI: 10.1016/j.inffus.2020.01.003 http://dx.doi.org/10.1016/j.inffus.2020.01.003 ]

LeCun Y and Bengio Y . 1995 . Convolutional networks for images , speech, and time series// Arbib M A ed . The Handbook of Brain Theory and Neural Networks. Cambridge, MA : MIT Press: 255 - 258 [ DOI: 10.5555/303568.303704 http://dx.doi.org/10.5555/303568.303704 ]

Ley A , D’Hondt O , Valade S , Hänsch R and Hellwich O . 2018 . Exploiting GAN-based SAR to optical image transcoding for improved classification via deep learning // Proceedings of the 12th European Conference on Synthetic Aperture Radar . Aachen : IEEE: 396 - 401

Li H and Wu X J . 2019 . DenseFuse: a fusion approach to infrared and visible images . IEEE Transactions on Image Processing , 28 ( 5 ): 2614 - 2623 [ DOI: 10.1109/TIP.2018.2887342 http://dx.doi.org/10.1109/TIP.2018.2887342 ]

Li H H , Guo L and Liu K . 2009 . SAR and optical image fusion based on curvelet transform . Journal of Optoelectronics·Laser , 20 ( 8 ): 1110 - 1113

李晖晖 , 郭雷 , 刘坤 . 2009 . 基于Curvelet变换的SAR与可见光图像融合研究. 光电子 . 激光 , 20 ( 8 ): 1110 - 1113 [ DOI: 10.3321/j.issn:1005-0086.2009.08.029 http://dx.doi.org/10.3321/j.issn:1005-0086.2009.08.029 ]

Li S T , Li C Y and Kang X D . 2021 . Development status and future prospects of multi-source remote sensing image fusion . National Remote Sensing Bulletin , 25 ( 1 ): 148 - 166

李树涛 , 李聪妤 , 康旭东 . 2021 . 多源遥感图像融合发展现状与未来展望 . 遥感学报 , 25 ( 1 ): 148 - 166 [ DOI: 10.11834/jrs.20210259 http://dx.doi.org/10.11834/jrs.20210259 ]

Li S Y , Zhang W F and Yang S . 2017 . Intelligence fusion method research of multisource high-resolution remote sensing images . Journal of Remote Sensing , 21 ( 3 ): 415 - 424

李盛阳 , 张万峰 , 杨松 . 2017 . 多源高分辨率遥感影像智能融合 . 遥感学报 , 21 ( 3 ): 415 - 424 [ DOI: 10.11834/jrs.20176386 http://dx.doi.org/10.11834/jrs.20176386 ]

Li X , Lei L , Sun Y L , Li M and Kuang G Y . 2020 . Multimodal bilinear fusion network with second-order attention-based channel selection for land cover classification . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 1011 - 1026 [ DOI: 10.1109/JSTARS.2020.2975252 http://dx.doi.org/10.1109/JSTARS.2020.2975252 ]

Liu L and Lei B . 2018 . Can SAR images and optical images transfer with each other? // 2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia : IEEE: 7019 - 7022 [ DOI: 10.1109/IGARSS.2018.8518921 http://dx.doi.org/10.1109/IGARSS.2018.8518921 ]

Liu Y , Chen X , Cheng J and Peng H . 2017a . A medical image fusion method based on convolutional neural networks // Proceedings of the 2017 20th International Conference on Information Fusion . Xi’an : IEEE: 1 - 7 [ DOI: 10.23919/ICIF.2017.8009769 http://dx.doi.org/10.23919/ICIF.2017.8009769 ]

Liu Y , Chen X , Peng H and Wang Z F . 2017b . Multi-focus image fusion with a deep convolutional neural network . Information Fusion , 36 : 191 - 207 [ DOI: 10.1016/j.inffus.2016.12.001 http://dx.doi.org/10.1016/j.inffus.2016.12.001 ]

Liu Y , Chen X , Wang Z F , Wang Z J , Ward R K and Wang X S . 2018 . Deep learning for pixel-level image fusion: recent advances and future prospects . Information Fusion , 42 : 158 - 173 [ DOI: 10.1016/j.inffus.2017.10.007 http://dx.doi.org/10.1016/j.inffus.2017.10.007 ]

Jung H , Kim Y , Jang H , Ha N and Sohn K . 2020 . Unsupervised deep image fusion with structure tensor representations . IEEE Transactions on Image Processing , 29 ( 99 ): 3845 - 3858 [ DOI: 10.1109/TIP.2020.2966075 http://dx.doi.org/10.1109/TIP.2020.2966075 ]

Ma J Y , Ma Y and Li C . 2019 . Infrared and visible image fusion methods and applications: a survey . Information Fusion , 45 : 153 - 178 [ DOI: 10.1016/j.inffus.2018.02.004 http://dx.doi.org/10.1016/j.inffus.2018.02.004 ]

Manaf S A , Mustapha N , Sulaiman M N , Husin N A and Hamid M R A . 2016 . Comparison of classification techniques on fused optical and SAR images for shoreline extraction: a case study at northeast coast of peninsular Malaysia . Journal of Computer Science , 12 ( 8 ): 399 - 411 [ DOI: 10.3844/jcssp.2016.399.411 http://dx.doi.org/10.3844/jcssp.2016.399.411 ]

Meraner A , Ebel P , Zhu X X and Schmitt M . 2020 . Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion . ISPRS Journal of Photogrammetry and Remote Sensing , 166 : 333 - 346 [ DOI: 10.1016/j.isprsjprs.2020.05.013 http://dx.doi.org/10.1016/j.isprsjprs.2020.05.013 ]

Mirza M and Osindero S . 2014 . Conditional generative adversarial nets . 2672 - 2680 . https://www.xueshufan.com/publication/2125389028 https://www.xueshufan.com/publication/2125389028 [ DOI: 10.48550/arXiv.1411.1784 http://dx.doi.org/10.48550/arXiv.1411.1784 ]

Pandit V R and Bhiwani R J . 2015 . Image fusion in remote sensing applications: a review . International Journal of Computer Applications , 120 ( 10 ): 22 - 32 [ DOI: 10.5120/21263-3846 http://dx.doi.org/10.5120/21263-3846 ]

Prabhakar K R , Srikar V S and Babu R V . 2017 . DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs // 2017 IEEE International Conference on Computer Vision (ICCV) . Venice : IEEE: 4724 - 4732 [ DOI: 10.1109/ICCV.2017.505 http://dx.doi.org/10.1109/ICCV.2017.505 ]

Ranchin T , Aiazzi B , Alparone L , Baronti S and Wald L . 2003 . Image fusion - The ARSIS concept and some successful implementation schemes . ISPRS Journal of Photogrammetry and Remote Sensing , 58 ( 1/2 ): 4 - 18 [ DOI: 10.1016/S0924-2716(03)00013-3 http://dx.doi.org/10.1016/S0924-2716(03)00013-3 ]

Scarpa G , Gargiulo M , Mazza A and Gaetano R . 2018 . A CNN-based fusion method for feature extraction from sentinel data . Remote Sensing , 10 ( 2 ): 236 [ DOI: 10.3390/rs10020236 http://dx.doi.org/10.3390/rs10020236 ]

Schmitt M , Hughes L H , Körner M and Zhu X X . 2018a . Colorizing sentinel-1 sar images using a variational autoencoder conditioned on sentinel-2 imagery . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII- 2 : 1045 - 1051 [ DOI: 10.5194/isprs-archives-XLII-2-1045-2018 http://dx.doi.org/10.5194/isprs-archives-XLII-2-1045-2018 ]

Schmitt M , Hughes L H , Qiu C and Zhu X X . 2019 . SEN12MS—A curated dataset of georeferenced multi-spectral Sentinel-1/2 imagery for deep learning and data fusion. ISPRSAnnals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W 7 : 153 - 160 [ DOI: 10.48550/arXiv.1906.07789 http://dx.doi.org/10.48550/arXiv.1906.07789 ]

Schmitt M , Hughes L H and Zhu X X . 2018b . The SEN1-2 dataset for deep learning in SAR-optical data fusion . ISPRSAnnals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV- 1 : 141 - 146 [ DOI: 10.5194/isprs-annals-IV-1-141-2018 http://dx.doi.org/10.5194/isprs-annals-IV-1-141-2018 ]

Schmitt M , Tupin F and Zhu X X . 2017 . Fusion of SAR and optical remote sensing data-challenges and recent trends // Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium . Fort Worth, TX : IEEE: 5458 - 5461 [ DOI: 10.1109/IGARSS.2017.8128239 http://dx.doi.org/10.1109/IGARSS.2017.8128239 ]

Shakya A , Biswas M and Pal M . 2020 . CNN-based fusion and classification of SAR and Optical data . International Journal of Remote Sensing , 41 ( 22 ): 8839 - 8861 [ DOI: 10.1080/01431161.2020.1783713 http://dx.doi.org/10.1080/01431161.2020.1783713 ]

Shao Z F , Wu W F and Guo S J . 2020 . IHS-GTF: a fusion method for optical and synthetic aperture radar data . Remote Sensing , 12 ( 17 ): 2796 [ DOI: 10.3390/rs12172796 http://dx.doi.org/10.3390/rs12172796 ]

Song X , Wu X J , Li H . 2019 . MSDNet for medical image fusion // International Conference on Image and Graphics . Beijing : Springer: 278 - 288 .[ DOI: 10.1007/978-3-030-34110-7_24 http://dx.doi.org/10.1007/978-3-030-34110-7_24 ]

Stathaki T . 2008 . Image Fusion: Algorithms and Applications . New York, USA : Academic Press ， 1 - 30

Sun C Q , Zhang C and Xiong , N X . 2020 . Infrared and visible image fusion techniques based on deep learning: a review . Electronics , 9 ( 12 ), 1 - 24 [ DOI: 10.3390/electronics9122162 http://dx.doi.org/10.3390/electronics9122162 ]

Toriya H , Dewan A and Kitahara I . 2019 . SAR2OPT: image alignment between multi-modal images using generative adversarial networks // 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 923 - 926 [ DOI: 10.1109/IGARSS.2019.8898605 http://dx.doi.org/10.1109/IGARSS.2019.8898605 ]

Wan J H , Zang J X and Liu S W . 2017 . Fusion and classification of SAR and optical image with consideration of polarization characteristics . Acta Optica Sinica , 37 ( 6 ): 0628001

万剑华 , 臧金霞 , 刘善伟 . 2017 . 顾及极化特征的SAR与光学影像融合与分类 . 光学学报 , 37 ( 6 ): 0628001 [ DOI: 10.3788/AOS201737.0628001 http://dx.doi.org/10.3788/AOS201737.0628001 ]

Wang X L and Chen C X . 2016 . Image fusion for synthetic aperture radar and multispectral images based on sub-band-modulated non-subsampled contourlet transform and pulse coupled neural network methods . The Imaging Science Journal , 64 ( 2 ): 87 - 93 [ DOI: 10.1080/13682199.2015.1136101 http://dx.doi.org/10.1080/13682199.2015.1136101 ]

Wang Y Y and Zhu X X . 2018 . The SARptical dataset for joint analysis of SAR and optical image in dense urban area // 2018 International Geoscience and Remote Sensing Symposium . Valencia : IEEE: 6840 - 6843 [ DOI: 10.1109/IGARSS.2018.8518298 http://dx.doi.org/10.1109/IGARSS.2018.8518298 ]

Xia Y , Zhang H Y , Zhang L P and Fan Z Y . 2019 . Cloud removal of optical remote sensing imagery with multitemporal Sar-optical data using X-Mtgan // 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 3396 - 3399 [ DOI: 10.1109/IGARSS.2019.8899105 http://dx.doi.org/10.1109/IGARSS.2019.8899105 ]

Xu J C and Cheng X J . 2015 . Comparative analysis of different fusion rules for SAR and multi-spectral image fusion based on NSCT and IHS transform // Proceedings of the 2015 International Conference on Computer and Computational Sciences . Greater Noida : IEEE: 271 - 274 [ DOI: 10.1109/ICCACS.2015.7361364 http://dx.doi.org/10.1109/ICCACS.2015.7361364 ]

Yang B , Zhong J Y , Li Y H and Chen Z Z . 2017 . Multi-focus image fusion and super-resolution with convolutional neural network . International Journal of Wavelets, Multiresolution and Information Processing , 15 ( 4 ): 1750037 [ DOI: 10.1142/S0219691317500370 http://dx.doi.org/10.1142/S0219691317500370 ]

Yi W , Zeng Y and Yuan Z . 2018 . Fusion of GF-3 SAR and optical images based on the nonsubsampled contourlet transform . Acta Optica Sinica , 38 ( 11 ): 1110002

易维 , 曾湧 , 原征 . 2018 . 基于NSCT变换的高分三号SAR与光学图像融合 . 光学学报 , 38 ( 11 ): 1110002 [ DOI: 10.3788/AOS201838.1110002 http://dx.doi.org/10.3788/AOS201838.1110002 ]

Zhang H , Shen H F and Zhang L P . 2016 . Fusion of multispectral and SAR images using sparse representation // 2016 IEEE International Geoscience and Remote Sensing Symposium . Beijing : IEEE: 7200 - 7203 [ DOI: 10.1109/IGARSS.2016.7730878 http://dx.doi.org/10.1109/IGARSS.2016.7730878 ]

Zhang H S , Wan L M , Wang T , Lin Y Y , Lin H and Zheng Z Z . 2019 . Impervious surface estimation from optical and polarimetric SAR data using small-patched deep convolutional networks: a comparative study . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12 ( 7 ): 2374 - 2387 [ DOI: 10.1109/JSTARS.2019.2915277 http://dx.doi.org/10.1109/JSTARS.2019.2915277 ]

Zhang J X , Zhou J J and Lu X W . 2020a . Feature-guided SAR-to-optical image translation . IEEE Access , 8 : 70925 - 70937 [ DOI: 10.1109/ACCESS.2020.2987105 http://dx.doi.org/10.1109/ACCESS.2020.2987105 ]

Zhang X C . 2021 . Benchmarking and comparing multi-exposure image fusion algorithms . Information Fusion , 74 : 111 - 131 [ DOI: 10.1016/j.inffus.2021.02.005 http://dx.doi.org/10.1016/j.inffus.2021.02.005 ]

Zhang X C , Ye P and Xiao G . 2020b . VIFB: a visible and infrared image fusion benchmark // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Seattle, WA : IEEE: 468 - 478 [ DOI: 10.1109/CVPRW50498.2020.00060 http://dx.doi.org/10.1109/CVPRW50498.2020.00060 ]

Zhao W Z , Qu Y , Chen J G and Yuan Z L . 2020 . Deeply synergistic optical and SAR time series for crop dynamic monitoring . Remote Sensing of Environment , 247 : 111952 [ DOI: 10.1016/j.rse.2020.111952 http://dx.doi.org/10.1016/j.rse.2020.111952 ]

Zhong J Y , Yang B , Huang G Y , Zhong F and Chen Z Z . 2016 . Remote sensing image fusion with convolutional neural network . Sensing and Imaging , 17 ( 1 ): 10 [ DOI: 10.1007/s11220-016-0135-6 http://dx.doi.org/10.1007/s11220-016-0135-6 ]

Zhou Y , Luo J C , Feng L , Yang Y P , Chen Y H and Wu W . 2019 . Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data . GIScience and Remote Sensing , 56 ( 8 ): 1170 - 1191 [ DOI: 10.1080/15481603.2019.1628412 http://dx.doi.org/10.1080/15481603.2019.1628412 ]

Zhu J Y , Park T , Isola P and Efros A A . 2017 . Unpaired image-to-image translation using cycle-consistent adversarial networks // Proceedings of the 2017 IEEE International Conference on Computer Vision . Venice : IEEE: 2242 - 2251 [ DOI: 10.1109/ICCV.2017.244 http://dx.doi.org/10.1109/ICCV.2017.244 ]

Zhu X X , Montazeri S , Ali M , Hua Y S , Wang Y Y , Mou L C , Shi Y L , Xu F and Bamler R . 2021 . Deep learning meets SAR: concepts, models, pitfalls, and perspectives . IEEE Geoscience and Remote Sensing Magazine , 9 ( 4 ): 143 - 172 [ DOI: 10.1109/MGRS.2020.3046356 http://dx.doi.org/10.1109/MGRS.2020.3046356 ]

文章被引用时，请邮件提醒。

提交

集成主体边缘分离和多尺度信息提取的双分支建筑物提取网络

跨视角图像地理定位数据集综述

联合双重注意力机制和双向特征金字塔的遥感影像小目标检测

空间信息感知语义分割模型的高分辨率遥感影像道路提取