سابِق|تال

PolSAR image deep learning super-resolution model based on multiscale attention mechanism

  • role： First author第一作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

  • Email：linliupeng@whu.edu.cn
  • Introduction：林镠鹏，研究方向为影像质量改善、数据融合等。E-mail： linliupeng@whu.edu.cn
LIN Liupeng1,  
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

LI Jie2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

    Hubei Luojia Laboratory, Wuhan 430079, China

  • Email：shenhf@whu.edu.cn
  • Introduction：沈焕锋，研究方向为资源环境遥感、多源数据融合、地学智能等。E-mail： shenhf@whu.edu.cn
SHEN Huanfeng13*

ملخص

Fully Polarimetric Synthetic Aperture Radar imagery (PolSAR) can provide rich polarimetric information; however, given the limitations of the system’s signal bandwidth and the physical size of the antenna, the spatial resolution of the SAR imaging system is restricted while acquiring multiple polarization information. To solve this problem, on the basis of the deep learning framework, this study proposes a multiscale attention-based PolSAR super-resolution network (MS-PSRN), which performs super-resolution reconstruction on the low-resolution full-polarimetric SAR images to generate the fully polarimetric SAR images with high spatial resolution. Under this super-resolution reconstruction framework, this study uses a multiscale architecture to fully extract the feature information of objects at different scales. On this basis, the spatial attention mechanism and the channel attention mechanism are introduced to recalibrate the feature maps, which are used to enhance the reconstruction performance of spatial details and improve the ability to maintain polarization information, respectively. Two attention mechanism embedding methods, i.e., joint and separated, are proposed to cope with the spatial size and quantity changes of the feature maps processed by the encoder and decoder. This study introduces a residual information distillation mechanism, extracts discriminative features through feature distillation, and compresses model parameters at the same time. In addition, the adaptive loss function is proposed to constrain the network training process and improve the model’s numerical fitting ability and edge information preservation ability. In this study, the proposed method is verified by two datasets, namely, the simulated data and the real data produced by RADARSAT-2 images. The experimental results of spatial information show that the proposed method is superior to the comparison algorithms in terms of visual results and quantitative indicators and has higher texture detail reconstruction accuracy and lower reconstruction error. The polarimetric information preservation test shows that the proposed method can effectively preserve the polarimetric information of PolSAR images while improving spatial resolution.

مفهوم

remote sensing;fully polarimetric synthetic aperture radar;super resolution;deep learning;multi-scale;attention mechanism

References

  1. 1.
    Chen J and Yang J. 2007. Super-resolution of polarimetric SAR images for ship detection//2007 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. Hangzhou: IEEE: 1499-1502
  2. 2.
    Fan C, Gong J Y, Zhu J J and Liao M S. 2009. Super-resolution reconstruction of ALOS-PRISM remote sensing images. Journal of Remote Sensing (in Chinese), 13(1): 75-82
  3. 3.
    Fu J, Liu J, Tian H J, Li Y, Bao Y J, Fang Z W and Lu H Q. 2019. Dual attention network for scene segmentation//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE: 3141-3149
  4. 4.
    He K M, Zhang X Y, Ren S Q and Sun J. 2015. Delving deep into rectifiers: surpassing human-level performance on imagenet classification//Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago: IEEE: 1026-1034
  5. 5.
    He Z and He D. 2020. Deep learning-based super-resolution for GF-4 satellite imagery. Journal of Remote Sensing (in Chinese), 24(12): 1500-1510
  6. 6.
    Kingma D P and Ba J. 2017. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
  7. 7.
    Lin L P, Li J, Yuan Q Q and Shen H F. 2019. Polarimetric SAR image super-resolution VIA deep convolutional neural network//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 3205-3208
  8. 8.
    Liu J, Tang J and Wu G S. 2020. Residual feature distillation network for lightweight image super-resolution//European Conference on Computer Vision. Glasgow: Springer: 41-55
  9. 9.
    Pastina D, Lombardo P, Farina A and Daddi P. 2001. Super-resolution of polarimetric SAR images of a ship//IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217). Sydney: IEEE: 2343-2345
  10. 10.
    Qi Z X, Yeh A G O, Li X and Lin Z. 2012. A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data. Remote Sensing of Environment, 118: 21-39
  11. 11.
    Shen H F, Lin L P, Li J, Yuan Q Q and Zhao L L. 2020. A residual convolutional neural network for polarimetric SAR image super-resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 90-108
  12. 12.
    Shi L, Sun W D, Yang J, Li P X and Lu L J. 2015. Building collapse assessment by the use of postearthquake Chinese VHR airborne SAR. IEEE Geoscience and Remote Sensing Letters, 12(10): 2021-2025
  13. 13.
    Suwa K and Iwamoto M. 2007. A two-dimensional bandwidth extrapolation technique for polarimetric synthetic aperture radar images. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 45-54
  14. 14.
    Wang F, Jiang M Q, Qian C, Yang S, Li C, Zhang H G, Wang X G and Tang X O. 2017. Residual attention network for image classification//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 6450-6458
  15. 15.
    Wang Y H and Liu H W. 2015. PolSAR ship detection based on superpixel-level scattering mechanism distribution features. IEEE Geoscience and Remote Sensing Letters, 12(8): 1780-1784
  16. 16.
    Yamaguchi Y, Moriyama T, Ishido M and Yamada H. 2005. Four-component scattering model for polarimetric SAR image decomposition. IEEE Transactions on Geoscience and Remote Sensing, 43(8): 1699-1706
  17. 17.
    Zamir S W, Arora A, Khan S, Hayat M, Khan F S, Yang M H and Shao L. 2021. Multi-stage progressive image restoration//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE: 14816-14826
  18. 18.
    Zeiler M D, Krishnan D, Taylor G W and Fergus R. 2010. Deconvolutional networks//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE: 2528-2535
  19. 19.
    Zhang L M, Zou B, Hao H J and Zhang Y. 2011. A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation. International Journal of Remote Sensing, 32(17): 4893-4913
  20. 20.
    Zhang T, Jiang L F, Xiang D L, Ban Y F, Pei L and Xiong H L. 2019. Ship detection from PolSAR imagery using the ambiguity removal polarimetric notch filter. ISPRS Journal of Photogrammetry and Remote Sensing, 157: 41-58
  21. 21.
    Zhang Y L, Li K P, Li K, Wang L C, Zhong B N and Fu Y. 2018a. Image super-resolution using very deep residual channel attention networks//Proceedings of the 15th European Conference on Computer Vision (ECCV). Munich: Springer: 294-310
  22. 22.
    Zhang Y L, Tian Y P, Kong Y, Zhong B N and Fu Y. 2018b. Residual dense network for image super-resolution//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 2472-2481
  23. 23.
    Zhao L L, Yang J, Li P X, Zhang L P, Shi L and Lang F K. 2013. Damage assessment in urban areas using post-earthquake airborne PolSAR imagery. International Journal of Remote Sensing, 34(24): 8952-8966
  24. 24.
    Zou B, Hao H J and Guo X J. 2008. Super-resolution of polarimetric SAR images based on target decomposition and polarimetric spatial correlation//IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. Boston: IEEE: II-911-II-914

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website