سابِق|تال

Airborne multidimensional SAR land cover dataset and fusion classification method

  • role： First author第一作者
  • Affiliation：

    Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

  • Email：nrzheng20@fudan.edu.cn
  • Introduction：郑乃榕，研究方向为合成孔径雷达图像的解译与应用。E-mail： nrzheng20@fudan.edu.cn
ZHENG Nairong1,  
  • Affiliation：

    Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

YANG Zi’an1,  
  • Affiliation：

    Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

SHI Xianzheng1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China

YANG Hong23,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

SUN Yue2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

  • Email：fengwang@fudan.edu.cn
  • Introduction：王峰，研究方向为智能遥感信息获取。E-mail： fengwang@fudan.edu.cn
WANG Feng1*

ملخص

With the development of Synthetic Aperture Radar (SAR) imaging and deep learning, the use of deep learning to classify land cover in SAR images has received extensive attention and applied research. In this study, a high-resolution airborne multidimensional SAR land cover classification dataset is constructed on the basis of the high-resolution airborne data of the Chinese Aeronautic Remote Sensing System (CARSS) for Earth observation, namely, AIR-MDSAR-Map (Airborne Multidimensional Synthetic Aperture Radar Mapping Dataset).The original data are obtained by CARSS, and the platform is a modified Xinzhou 60 remote sensing aircraft. SAR and optical images are generated in accordance with the standard data production process. After imaging processing, radiometric correction, polarization correction, and geometric correction, the original SAR data are preprocessed to form Single Look Complex (SLC) data, and then geometric processing is used to generate SAR DOM products. After image enhancement, splicing, and rough correction, the raw optical data are preprocessed to generate DSM data, and then semiautomatic filtering is performed to produce DEM. Finally, AIR-MDSAR-Map contains polarization SAR images in bands of C, Ka, L, P, and S and high-resolution optical images in Wanning, Hainan, and Sheyang, Jiangsu, with the spatial resolution ranging from 0.2 m to 1 m depending on the band.AIR-MDSAR-Map divides the land cover into nine categories and generates fine pixel-level labels through a semiautomatic labeling algorithm. In this study, the classical semantic segmentation methods in deep learning, such as UNet, SegNet, DeepLab, and HRNet, are used to verify the classification of AIR-MDSAR-Map. At the same time, we test the classification sensitivity of different band images to all kinds of land cover objects.This dataset includes multidimensional SAR images of the same place and time, which can be used for fusion classification research. In this study, multidimensional SAR data are fused and classified through different fusion strategies; model fusion classifies land cover by selectively fusing the models of each band, and the a priori fusion uses the prior information of the classification results in each band to distinguish land cover on defining the priority of objects. These two fusion methods outperform the single band in the performance of some types of land cover and improve the FWIoU and PA by 10%—15%, the FWIoU reaches 69%, and PA is 81%.AIR-MDSAR-Map can satisfy the research and application requirements of different users and can be used to study the characteristics of the same land cover object with different resolutions, bands, and polarizations. Moreover, it can provide a strong promotion for the development of multidimensional SAR applications. The AIR-MDSAR-Map will be available at the ChinaGEOSS Data Sharing Network (http://www.chinageoss.cn).

مفهوم

remote sensing;airborne SAR;multi-dimensional;land cover classification;deep learning;semantic segmentation;AIR-MDSAR-Map

References

  1. 1.
    Addabbo P, Focareta M, Marcuccio S, Votto C and Ullo S L. 2016. Land cover classification and monitoring through multisensor image and data combination//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE: 902-905
  2. 2.
    Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495
  3. 3.
    Cen Y, Zhang L F, Zhang X, Wang Y M, Qi W C, Tang S L and Zhang P. 2020. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village). Journal of Remote Sensing (in Chinese), 24(11): 1299-1306
  4. 4.
    Chen B M and Zhou X P. 2007. Explanation of current land use condition classification for national standard of the People’s Republic of China. Journal of Natural Resources, 22(6): 994-1003
  5. 5.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018b. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
  6. 6.
    Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018a. Encoder-decoder with atrous separable convolution for semantic image segmentation//15th European Conference on Computer Vision. Munich: Springer: 833-851
  7. 7.
    Chen Q, Kuang G Y, Li J, Sui L and Li D G. 2013. Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1817-1825
  8. 8.
    Cloude S R and Pottier E. 1996. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 498-518
  9. 9.
    Deng J, Dong W, Socher R, Li L J, Li K and Fei-Fei L. 2009. ImageNet: a large-scale hierarchical image database//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE: 248-255
  10. 10.
    Dierking W and Busche T. 2006. Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 957-970
  11. 11.
    Fan J Y, Ke C Q, Yao G H and Wang Z F. 2023. Identification of glaciers using fully polarimetric SAR data based on deep-learning. National Remote Sensing Bulletin, 27(9): 2098-2113
  12. 12.
    Freeman A and Durden S L. 1998. A three-component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36(3): 963-973
  13. 13.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 770-778
  14. 14.
    Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, Andreetto M and Adam H. 2017. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: 1704.04861
  15. 15.
    Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE: 2261-2269
  16. 16.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE: 3431-3440
  17. 17.
    Lv Q, Dou Y, Niu X, Xu J Q, Xu J B and Xia F. 2015. Urban land use and land cover classification using remotely sensed SAR data through deep belief networks. Journal of Sensors, 2015: 538063
  18. 18.
    Mishra P and Singh D. 2014. A statistical-measure-based adaptive land cover classification algorithm by efficient utilization of polarimetric SAR observables. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2889-2900
  19. 19.
    Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I and Papathanassiou K P. 2013. A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1): 6-43
  20. 20.
    O’Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez G V, Krpalkova L, Riordan D and Walsh J. 2020. Deep learning vs. traditional computer vision//Advances in Computer Vision. Cham: Springer: 128-144
  21. 21.
    Ronneberger O, Fischer P and Brox T. 2015. U-net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241
  22. 22.
    Shimoni M, Borghys D, Heremans R, Perneel C and Acheroy M. 2009. Fusion of PolSAR and PolInSAR data for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(3): 169-180
  23. 23.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition//3rd International Conference on Learning Representations. San Diego: [s.n.]
  24. 24.
    Song Y H and Hao Y. 2017. Image segmentation algorithms overview. arXiv preprint arXiv: 1707.02051
  25. 25.
    Sukawattanavijit C, Chen J and Zhang H S. 2017. GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(3): 284-288
  26. 26.
    Sun K, Xiao B, Liu D and Wang J D. 2019. Deep high-resolution representation learning for human pose estimation//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE: 5686-5696
  27. 27.
    Tong X Y, Xia G S, Lu Q K, Shen H F, Li S Y, You S C and Zhang L P. 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237: 111322
  28. 28.
    Wang J D, Sun K, Cheng T H, Jiang B R, Deng C R, Zhao Y, Liu D, Mu Y D, Tan M K, Wang X G, Liu W Y and Xiao B. 2021. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10): 3349-3364
  29. 29.
    Wang Z R, Zeng X, Yan Z Y, Kang J and Sun X. 2022. AIR-PolSAR-Seg: a large-scale data set for terrain segmentation in complex-scene PolSAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 3830-3841
  30. 30.
    Wei D, Li Y and Huang D. 2020. Overview on methods of land classification based on polarimetric SAR images. Computer Systems and Applications, 29(11): 29-39
  31. 31.
    Wu F, Zhang H, Wang C, Li L, Li J J, Chen W R and Zhang B. 2022. SARBuD1.0: a SAR building dataset based on GF-3 FSII imageries for built-up area extraction with deep learning method. National Remote Sensing Bulletin, 26(4): 620-631
  32. 32.
    Wu Y R. 2013. Concept on multidimensional space joint-observation SAR. Journal of Radars, 2(2): 135-142
  33. 33.
    Zhao Q, Liu J H, Li Y W and Zhang H. 2022. Semantic segmentation with attention mechanism for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5403913
  34. 34.
    Zheng N R, Yang Z A, Yang H, Sun Y and Wang F. 2023. Semi-automatic annotation method for semantic segmentation of synthetic aperture radar images//Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022). Singapore: Springer: 95-101
  35. 35.
    Zhong Y F, Hu X, Luo C, Wang X Y, Zhao J and Zhang L P. 2020. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF. Remote Sensing of Environment, 250: 112012

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