Citation: | LI Mingdian, XIAO Shunping, CHEN Siwei. A Review of Progress in Super-Resolution Reconstruction of Polarimetric Radar Image Target[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1806-1826. doi: 10.11999/JEIT231249 |
[1] |
陈思伟. 成像雷达极化旋转域解译理论与应用[M]. 北京: 科学出版社, 2023: 1–251.
CHEN Siwei. Imaging Radar Polarimetric Rotation Domain Interpretation[M]. Beijing: Science Press, 2023: 1–251.
|
[2] |
CHEN Siwei, WANG Xuesong, XIAO Shunping, et al. Target Scattering Mechanism in Polarimetric Synthetic Aperture Radar[M]. Singapore: Springer, 2018: 1–225. doi: 10.1007/978-981-10-7269-7.
|
[3] |
李宁, 牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法[J]. 雷达学报, 2020, 9(1): 174–184. doi: 10.12000/JR19096.
LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096.
|
[4] |
陈嘉琪, 刘祥梅, 李宁, 等. 一种超分辨SAR图像水域分割算法及其应用[J]. 电子与信息学报, 2021, 43(3): 700–707. doi: 10.11999/jeit200366.
CHEN Jiaqi, LIU Xiangmei, LI Ning, et al. A high-precision water segmentation algorithm for SAR image and its application[J]. Journal of Electronics & Information Technology, 2021, 43(3): 700–7076. doi: 10.11999/jeit200366.
|
[5] |
SHEN Huanfeng, LIN Liupeng, LI Jie, et al. A residual convolutional neural network for polarimetric SAR image super-resolution[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 90–108. doi: 10.1016/j.isprsjprs.2020.01.006.
|
[6] |
SHI Xiaoran, ZHOU Feng, YANG Shuang, et al. Automatic target recognition for synthetic aperture radar images based on super-resolution generative adversarial network and deep convolutional neural network[J]. Remote Sensing, 2019, 11(2): 135. doi: 10.3390/rs11020135.
|
[7] |
ZHANG Chongqi, ZHANG Ziwen, DENG Yao, et al. Blind super-resolution for SAR images with speckle noise based on deep learning probabilistic degradation model and SAR priors[J]. Remote Sensing, 2023, 15(2): 330. doi: 10.3390/rs15020330.
|
[8] |
MEN Peng, GUO Hao, AN Jubai, et al. Large-resolution difference heterogeneous SAR image sea ice drift tracking using a smooth edge-guide Super-resolution residual network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5212519. doi: 10.1109/tgrs.2023.3296462.
|
[9] |
高勋章, 张志伟, 刘梅, 等. 雷达像智能识别对抗研究进展[J]. 雷达学报, 2023, 12(4): 696–712. doi: 10.12000/jr23098.
GAO Xunzhang, ZHANG Zhiwei, LIU Mei, et al. Intelligent radar image recognition countermeasures: A review[J]. Journal of Radars, 2023, 12(4): 696–712. doi: 10.12000/jr23098.
|
[10] |
CHEN Siwei and TAO Chensong. PolSAR image classification using polarimetric-feature-driven deep convolutional neural network[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4): 627–631. doi: 10.1109/LGRS.2018.2799877.
|
[11] |
LI Mingdian, XIAO Shunping, and CHEN Siwei. Three-dimension polarimetric correlation pattern interpretation tool and its application[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5238716. doi: 10.1109/TGRS.2022.3222691.
|
[12] |
CHEN Siwei, LI Mingdian, CUI Xingchao, et al. Polarimetric roll-invariant features and applications for polarimetric synthetic aperture radar ship detection: A comprehensive summary and investigation[J]. IEEE Geoscience and Remote Sensing Magazine, 2024, 12(1): 36–66. doi: 10.1109/MGRS.2023.3328472.
|
[13] |
CHEN Siwei. Polarimetric coherence pattern: A visualization and characterization tool for PolSAR data investigation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 286–297. doi: 10.1109/TGRS.2017.2746662.
|
[14] |
CHEN Siwei, WANG Xuesong, and SATO M. Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3. 11 east Japan earthquake[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 6919–6929. doi: 10.1109/TGRS.2016.2588325.
|
[15] |
LI Mingdian, CUI Xingchao, YANG Chengli, et al. Polarimetric ISAR super-resolution based on group residual attention network[C]. 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN), Zhangye, China, 2022: 566–571. doi: 10.1109/ICICN56848.2022.10006508.
|
[16] |
LI Mingdian, DENG Junwu, XIAO Shunping, et al. Semi-supervised implicit neural representation for polarimetric ISAR image super-resolution[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3505505. doi: 10.1109/LGRS.2023.3287283.
|
[17] |
LI Mingdian, DENG Junwu, XIAO Shunping, et al. NLSAN: A non-local scene awareness network for compact polarimetric ISAR image super-resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5111416. doi: 10.1109/TGRS.2023.3331822.
|
[18] |
崔兴超, 李郝亮, 付耀文, 等. 空间目标散射结构极化旋转域辨识[J]. 电子与信息学报, 2022, 45(6): 2105–2114. doi: 10.11999/JEIT220493.
CUI Xingchao, LI Haoliang, FU Yaowen, et al. Scattering structure recognition of space target in polarimetric rotation domain[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2105–2114. doi: 10.11999/JEIT220493.
|
[19] |
李郝亮, 陈思伟. 海面角反射体电磁散射特性与雷达鉴别研究进展与展望[J]. 雷达学报, 2023, 12(4): 738–761. doi: 10.12000/jr23100.
LI Haoliang and CHEN Siwei. Electromagnetic scattering characteristics and radar identification of sea corner reflectors: Advances and prospects[J]. Journal of Radars, 2023, 12(4): 738–761. doi: 10.12000/JR23100. doi: 10.12000/jr23100.
|
[20] |
许璐, 张红, 王超, 等. 简缩极化SAR数据处理与应用研究进展[J]. 雷达学报, 2020, 9(1): 55–72. doi: 10.12000/JR19106.
XU Lu, ZHANG Hong, WANG Chao, et al. Progress in the processing and application of compact polarimetric SAR[J]. Journal of Radars, 2020, 9(1): 55–72. doi: 10.12000/JR19106.
|
[21] |
CZERWINSKI M G and USOFF J M. Development of the haystack ultrawideband satellite imaging radar[J]. Lincoln Laboratory Journal, 2014, 21(1): 28–44.
|
[22] |
呼鹏江. 空天目标逆合成孔径雷达精细成像技术研究[D]. [博士论文], 国防科技大学, 2018. doi: 10.27052/d.cnki.gzjgu.2018.000338.
HU Pengjiang. Research on inverse synthetic aperture radar fine imaging technology of aerospace targets[D]. [Ph. D. dissertation], National University of Defense Technology, 2018. doi: 10.27052/d.cnki.gzjgu.2018.000338.
|
[23] |
CUMMING I G, WONG F H, 洪文, 胡东辉, 译. 合成孔径雷达成像: 算法与实现[M]. 北京: 电子工业出版社, 2007: 1–428.
CUMMING I G, WONG F H, HONG Wen and HU Donghui. translation. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation[M]. Beijing: Publishing House of Electronics Industry, 2007: 1–428.
|
[24] |
SOUMEKH M. Synthetic Aperture Radar Signal Processing with MATLAB Algorithms[M]. New York: Jone Wiley & Sons, 1999: 1–648.
|
[25] |
BORISON S L, BOWLING S B, and CUOMO K M. Super-resolution methods for wideband radar[J]. The Lincoln Laboratory Journal, 1992, 5(3): 441–461.
|
[26] |
JONSSON R. Regularization based super resolution imaging using FFT: s[C]. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, Orlando, USA, 2005: 122–131. doi: 10.1117/12.604347.
|
[27] |
FARINA A, PRODI F, and VINELLI F. Application of superresolution techniques to radar imaging[J]. Journal of Systems Engineering and Electronics, 1994, 5(1): 1–14.
|
[28] |
冉承其, 王正明. 基于基追踪的SAR图像超分辨处理[J]. 宇航学报, 2006, 27(1): 51–56. doi: 10.3321/j.issn:1000-1328.2006.01.011.
RAN Chengqi and WANG Zhengming. Super-resolution processing of SAR image by basis pursuit method[J]. Journal of Astronautics, 2006, 27(1): 51–56. doi: 10.3321/j.issn:1000-1328.2006.01.011.
|
[29] |
ZHANG Ping and YANG Ruliang. A new superresolution SAR imaging algorithm based on extrapolation[C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: 407–410. doi: 10.1109/IGARSS.2008.4779744.
|
[30] |
HE Chu, LIU Longzhu, XU Lianyu, et al. Learning based compressed sensing for SAR image super-resolution[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(4): 1272–1281. doi: 10.1109/jstars.2012.2189555.
|
[31] |
DU Chuan, XIE Pengfei, ZHANG Lei, et al. Conditional prior probabilistic generative model with similarity measurement for ISAR imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4013205. doi: 10.1109/LGRS.2021.3073691.
|
[32] |
HARRIS J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 1964, 54(7): 931–936. doi: 10.1364/josa.54.000931.
|
[33] |
MOSER B B, RAUE F, FROLOV S, et al. Hitchhiker's guide to super-resolution: Introduction and recent advances[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9862–9882. doi: 10.1109/TPAMI.2023.3243794.
|
[34] |
WANG Zhihao, CHEN Jian, and HOI S C H. Deep learning for image super-resolution: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3365–3387. doi: 10.1109/TPAMI.2020.2982166.
|
[35] |
邢孟道, 保铮, 李真芳, 等. 雷达成像算法进展[M]. 北京: 电子工业出版社, 2014: 1–262.
XING Mengdao, BAO Zheng, LI Zhenfang, et al. Progress of Radar Imaging Algorithm[M]. Beijing: Publishing House of Electronics Industry, 2014: 1–262.
|
[36] |
LIPPS R and KERR D. Polar reformatting for ISAR imaging[C]. 1998 IEEE Radar Conference, RADARCON'98. Challenges in Radar Systems and Solutions, Dallas, USA 1998: 275–280. doi: 10.1109/NRC.1998.678014.
|
[37] |
朱正为, 周建江. 基于联合聚焦/超分辨贝叶斯模型的雷达目标超分辨重建[J]. 系统工程与电子技术, 2011, 33(6): 1261–1264. doi: 10.3969/j.issn.1001-506X.2011.06.13.
ZHU Zhengwei and ZHOU Jianjiang. Radar target image super-resolution reconstruction based on bayesian joint focus/super-resolution model[J]. Systems Engineering and Electronics, 2011, 33(6): 1261–1264. doi: 10.3969/j.issn.1001-506X.2011.06.13.
|
[38] |
张倩. SAR图像质量评估及其目标识别应用[D]. [博士论文], 中国科学技术大学, 2011.
ZHANG Qian. Quality assessment and target recognition in SAR images[D]. [Ph. D. dissertation], University of Science and Technology of China, 2011.
|
[39] |
王光新, 王正明, 王卫威. 基于Cauchy稀疏分布的SAR图像超分辨算法[J]. 宇航学报, 2008, 29(1): 299–303. doi: 10.3873/j.issn.1000-1328.2008.01.054.
WANG Guangxin, WANG Zhengming, and WANG Weiwei. SAR image super-resolution based on sparse cauchy distribution[J]. Journal of Astronautics, 2008, 29(1): 299–303. doi: 10.3873/j.issn.1000-1328.2008.01.054.
|
[40] |
汪雄良, 王正明, 赵侠, 等. 基于lk范数正则化方法的SAR图像超分辨[J]. 宇航学报, 2005, 26(S1): 77–82. doi: 10.3321/j.issn:1000-1328.2005.z1.015.
WANG Xiongliang, WANG Zhengming, ZHAO Xia, et al. SAR image super-resolution based on regularization of lk norm[J]. Journal of Astronautics, 2005, 26(S1): 77–82. doi: 10.3321/j.issn:1000-1328.2005.z1.015.
|
[41] |
曲长文, 徐舟, 陈天乐. 稀疏条件下基于散射点估计的SAR切片超分辨重建[J]. 电子与信息学报, 2015, 37(1): 71–77. doi: 10.11999/JEIT140121.
QU Changwen, XU Zhou, and CHEN Tianle. Super-resolution reconstruction of SAR section based on scattering center estimation and sparse constraint[J]. Journal of Electronics & Information Technology, 2015, 37(1): 71–77. doi: 10.11999/JEIT140121.
|
[42] |
QI Mingrui, CHEN Chen, and YANG Qingwei. Single ISAR image enhancement based on convolutional neural network[C]. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), Hong Kong, China, 2022: 121665R. doi: 10.1117/12.2617683.
|
[43] |
ADDABBO P, BERNARDI M L, BIONDI F, et al. Super-resolution of synthetic aperture radar complex data by deep-learning[J]. IEEE Access, 2023, 11: 23647–23658. doi: 10.1109/ACCESS.2023.3251565.
|
[44] |
YANG Ting, SHI Hongyin, LANG Manyun, et al. ISAR imaging enhancement: Exploiting deep convolutional neural network for signal reconstruction[J]. International Journal of Remote Sensing, 2020, 41(24): 9447–9468. doi: 10.1080/01431161.2020.1799449.
|
[45] |
QIN Dan, LIU Diyang, GAO Xunzhang, et al. ISAR resolution enhancement using residual network[C]. 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2019: 788–792. doi: 10.1109/SIPROCESS.2019.8868757.
|
[46] |
易拓源, 户盼鹤, 刘振. 基于改进CycleGAN的ISAR图像超分辨方法[J]. 信号处理, 2023, 39(2): 323–334. doi: 10.16798/j.issn.1003-0530.2023.02.013.
YI Tuoyuan, HU Panhe, and LIU Zhen. ISAR images super-resolution method based on ameliorated CycleGAN[J]. Journal of Signal Processing, 2023, 39(2): 323–334. doi: 10.16798/j.issn.1003-0530.2023.02.013.
|
[47] |
ZHOU Peng, MARTORELLA M, ZHANG Xi, et al. Circular scan ISAR mode super-resolution imaging of ships based on a combination of data extrapolation and compressed sensing[J]. IEEE Sensors Journal, 2019, 19(16): 6883–6894. doi: 10.1109/JSEN.2019.2913903.
|
[48] |
GIUSTI E, CATALDO D, BACCI A, et al. ISAR image resolution enhancement: Compressive sensing versus state-of-the-art super-resolution techniques[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(4): 1983–1997. doi: 10.1109/taes.2018.2807283.
|
[49] |
WANG Haobo, LI Kaiming, LU Xiaofei, et al. ISAR resolution enhancement method exploiting generative adversarial network[J]. Remote Sensing, 2022, 14(5): 1291. doi: 10.3390/rs14051291.
|
[50] |
WANG Yong, JI Bingren, ZHAO Bin, et al. A novel super-resolution ISAR imaging method based on the EM-turbo technique[J]. Remote Sensing Letters, 2022, 13(8): 844–853. doi: 10.1080/2150704x.2022.2091962.
|
[51] |
LEE S J and LEE S G. Super-resolution procedure for target responses in KOMPSAT-5 images[J]. Sensors, 2022, 22(19): 7189. doi: 10.3390/s22197189.
|
[52] |
LIN Liupeng, SHEN Huanfeng, LI Jie, et al. FDFNet: A fusion network for generating high-resolution fully polSAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4500905. doi: 10.1109/lgrs.2021.3127958.
|
[53] |
LIN Liupeng, LI Jie, SHEN Huanfeng, et al. Low-resolution fully polarimetric SAR and high-resolution single-polarization SAR image fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5216117. doi: 10.1109/TGRS.2021.3121166.
|
[54] |
SUWA K and IWAMOTO M. A two-dimensional bandwidth extrapolation technique for polarimetric synthetic aperture radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 45–54. doi: 10.1109/TGRS.2006.885406.
|
[55] |
WEI Yangkai, LI Yinchuan, DING Zegang, et al. SAR parametric super-resolution image reconstruction methods based on ADMM and deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 10197–10212. doi: 10.1109/TGRS.2021.3052793.
|
[56] |
李春茂, 吴顺君, 张林让. 基于边缘保持的自适应SAR图像超分辨重建算法[J]. 系统仿真学报, 2014, 26(2): 254–259. doi: 10.16182/j.cnki.joss.2014.02.020.
LI Chunmao, WU Shunjun, and ZHANG Linrang. Adaptive edge-preserving based reconstruction algorithm for SAR images[J]. Journal of System Simulation, 2014, 26(2): 254–259. doi: 10.16182/j.cnki.joss.2014.02.020.
|
[57] |
李萌, 刘畅. 基于特征复用的膨胀-残差网络的SAR图像超分辨重建[J]. 雷达学报, 2020, 9(2): 363–372. doi: 10.12000/JR19110.
LI Meng and LIU Chang. Super-resolution reconstruction of SAR images based on feature reuse dilated-residual convolutional neural networks[J]. Journal of Radars, 2020, 9(2): 363–372. doi: 10.12000/JR19110.
|
[58] |
LI Yanshan, XU Fan, ZHOU Li, et al. Optical-guided residual learning network for synthetic aperture radar image super-resolution[J]. Journal of Applied Remote Sensing, 2022, 16(3): 036503. doi: 10.1117/1.Jrs.16.036503.
|
[59] |
KANAKARAJ S, NAIR M S, and KALADY S. SAR image super resolution using importance sampling unscented kalman filter[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2): 562–571. doi: 10.1109/jstars.2017.2779795.
|
[60] |
WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612. doi: 10.1109/TIP.2003.819861.
|
[61] |
KANAKARAJ S, NAIR M S, and KALADY S. Adaptive importance sampling unscented kalman filter with kernel regression for SAR image super-resolution[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4004305. doi: 10.1109/lgrs.2020.3031600.
|
[62] |
KANAKARAJ S, NAIR M S, and KALADY S. Adaptive importance sampling unscented kalman filter based SAR image super resolution[J]. Computers & Geosciences, 2019, 133: 104310. doi: 10.1016/j.cageo.2019.104310.
|
[63] |
LIN Zhang, ZHANG Lei, MOU Xuanqin, et al. FSIM: A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378–2386. doi: 10.1109/TIP.2011.2109730.
|
[64] |
SATTAR F, FLOREBY L, SALOMONSSON G, et al. Image enhancement based on a nonlinear multiscale method[J]. IEEE Transactions on Image Processing, 1997, 6(6): 888–895. doi: 10.1109/83.585239.
|
[65] |
LEE J S, POTTIER E, 洪文, 李洋, 尹嫱, 等译. 极化雷达成像基础与应用[M]. 北京: 电子工业出版社, 2013: 1–312.
LEE J S POTTIER E, HONG Wen, LI Yang, YIN Qiang, et al. translation. Polarimetric Radar Imaging From Basics to Applications[M]. Beijing: Publishing House of Electronics Industry, 2013: 1–312.
|
[66] |
吴永辉, 郁文贤, 计科峰, 等. 基于修正H-α方法的双极化SAR散射机理识别性能分析[J]. 电子学报, 2010, 38(3): 572–579.
WU Yonghui, YU Wenxian, JI Kefeng, et al. Performance analysis of scattering mechanism identification of dual-polarization SAR using a modified H-α decomposition[J]. Acta Electronica Sinica, 2010, 38(3): 572–579.
|
[67] |
孙即祥. 现代模式识别[M]. 北京: 高等教育出版社, 2008: 186, 187.
SUN Jixiang. Modern Pattern Recognition[M]. Beijing: Higher Education Press, 2008: 186, 187.
|
[68] |
LIU Lu, HUANG Wei, and WANG Cheng. Texture image prior for SAR image super resolution based on total variation regularization using split Bregman iteration[J]. International Journal of Remote Sensing, 2017, 38(20): 5673–5687. doi: 10.1080/01431161.2017.1346325.
|
[69] |
GOLDSTEIN T and OSHER S. The split bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 323–343. doi: 10.1137/080725891.
|
[70] |
DONOHO D L. Sparse components of images and optimal atomic decompositions[J]. Constructive Approximation, 2001, 17(3): 353–382. doi: 10.1007/s003650010032.
|
[71] |
DONG Chao, LOY C C, HE Kaiming, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295–307. doi: 10.1109/TPAMI.2015.2439281.
|
[72] |
陈思伟, 李永祯, 王雪松, 等. 极化SAR目标散射旋转域解译理论与应用[J]. 雷达学报, 2017, 6(5): 442–455. doi: 10.12000/JR17033.
CHEN Siwei, LI Yongzhen, WANG Xuesong, et al. Polarimetric SAR target scattering interpretation in rotation domain: Theory and application[J]. Journal of Radars, 2017, 6(5): 442–455. doi: 10.12000/JR17033.
|
[73] |
CEN Xi, SONG Xuan, LI Yachao, et al. A deep learning-based super-resolution model for bistatic SAR image[C]. 2021 International Conference on Electronics, Circuits and Information Engineering (ECIE), Zhengzhou, China, 2021: 228–233. doi: 10.1109/ECIE52353.2021.00056.
|
[74] |
AO Dongyang, DUMITRU C O, SCHWARZ G, et al. Dialectical GAN for SAR image translation: From Sentinel-1 to TerraSAR-X[J]. Remote Sensing, 2018, 10(10): 1597. doi: 10.3390/rs10101597.
|
[75] |
QIN Dan and GAO Xunzhang. Enhancing ISAR resolution by a generative adversarial network[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(1): 127–131. doi: 10.1109/LGRS.2020.2965743.
|
[76] |
LIN Liupeng, LI Jie, YUAN Qiangqiang, et al. Polarimetric SAR image super-resolution via deep convolutional neural network[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 3205–3208. doi: 10.1109/IGARSS.2019.8898160.
|
[77] |
林镠鹏, 李杰, 沈焕锋. 基于多尺度注意力机制的PolSAR深度学习超分辨率模型[J]. 遥感学报, 预初版. doi: 10.11834/jrs.20233002.
LIN Liupeng, LI Jie, and SHEN Huanfeng. PolSAR image deep learning super-resolution model based on multi-scale attention mechanism[J]. National Remote Sensing Bulletin, in press. doi: 10.11834/jrs.20233002.
|
[78] |
CHEN Yinbo, LIU Sifei, and WANG Xiaolong. Learning continuous image representation with local implicit image function[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 8624–8634. doi: 10.1109/CVPR46437.2021.00852.
|
[79] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
|
[80] |
ZHU Hongyu, XIE Chao, FEI Yeqi, et al. Attention mechanisms in CNN-based single image super-resolution: A brief review and a new perspective[J]. Electronics, 2021, 10(10): 1187. doi: 10.3390/electronics10101187.
|
[81] |
GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]. 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 2672–2680.
|
[82] |
LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 105–114. doi: 10.1109/CVPR.2017.19.
|
[83] |
WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: Enhanced super-resolution generative adversarial networks[C]. Computer Vision – ECCV 2018 Workshops, Munich, Germany, 2019: 63–79. doi: 10.1007/978-3-030-11021-5_5.
|
[84] |
ZHANG Lamei, ZOU Bin, HAO Huijun, et al. A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation[J]. International Journal of Remote Sensing, 2011, 32(17): 4893–4913. doi: 10.1080/01431161.2010.492251.
|
[85] |
PASTINA D, LOMBARDO P, FARINA A, et al. Super-resolution of polarimetric SAR images of a ship[C]. IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Australia, 2001: 2343–2345. doi: 10.1109/IGARSS.2001.977996.
|
[86] |
NOVAK L M and BURL M C. Optimal speckle reduction in polarimetric SAR imagery[C]. Twenty-Second Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 1988: 781–793. doi: 10.1109/ACSSC.1988.754657.
|
[87] |
CHEN Jiong and YANG Jian. Super-resolution of polarimetric SAR images for ship detection[C]. 2007 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, Hangzhou, China, 2007: 1499–1502. doi: 10.1109/MAPE.2007.4393565.
|
[88] |
STARK H and OSKOUI P. High-resolution image recovery from image-plane arrays, using convex projections[J]. Journal of the Optical Society of America A, 1989, 6(11): 1715–1726. doi: 10.1364/josaa.6.001715.
|
[89] |
CAO Yue, XU Jiarui, LIN S, et al. GCNet: Non-local networks meet squeeze-excitation networks and beyond[C]. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019: 1971–1980. doi: 10.1109/ICCVW.2019.00246.
|
[90] |
VAN DER MAATEN L and HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579–2605.
|