Volume 14 Issue 2
Apr.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(2): 353-365
WANG Zhirui, ZHAO Liangjin, WANG Yuelei, et al. AIR-PolSAR-Seg-2.0: Polarimetric SAR ground terrain classification dataset for large-scale complex scenes[J]. Journal of Radars, 2025, 14(2): 353–365. doi: 10.12000/JR24237
Citation: WANG Zhirui, ZHAO Liangjin, WANG Yuelei, et al. AIR-PolSAR-Seg-2.0: Polarimetric SAR ground terrain classification dataset for large-scale complex scenes[J]. Journal of Radars, 2025, 14(2): 353–365. doi: 10.12000/JR24237
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AIR-PolSAR-Seg-2.0: Polarimetric SAR Ground Terrain Classification Dataset for Large-scale Complex Scenes

DOI: 10.12000/JR24237 CSTR: 32380.14.JR24237
  • 1.

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

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 4.

    Key Laboratory of Target Cognition and Application Technology (TCAT), Beijing 100190, China

  • 5.

    School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China

  • 6.

    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Funds:  The National Natural Science Foundation of China (62331027)
More Information
  • Corresponding author: Zhao Liangjin, zhaolj004896@aircas.ac.cn
  • Received Date: 2024-11-29
  • Rev Recd Date: 2025-03-23
    • Available Online: 2025-03-24
  • Publish Date: 2025-03-31
    Abstract
  • The ground terrain classification using Polarimetric Synthetic Aperture Radar (PolSAR) is one of the research hotspots in the field of intelligent interpretation of SAR images. To further promote the development of research in this field, this paper organizes and releases a polarimetric SAR ground terrain classification dataset named AIR-PolSAR-Seg-2.0 for large-scale complex scenes. This dataset is composed of three L1A-level complex SAR images of the Gaofen-3 satellite from different regions, with a spatial resolution of 8 meters. It includes four polarization modes: HH, HV, VH, VV, and covers six typical ground terrain categories such as water bodies, vegetation, bare land, buildings, roads, and mountains. It has the characteristics of large-scale complex scenes, diverse strong and weak scattering, irregular boundary distribution, diverse category scales, and unbalanced sample distribution. To facilitate experimental verification, this paper cuts the three complete SAR images into 24,672 slices of 512×512 pixels, and conducts experimental verification using a series of common deep learning methods. The experimental results show that the DANet based on the dual-channel self-attention method performs the best, with the average intersection over union ratio reaching 85.96% for amplitude data and 87.03% for amplitude-phase fusion data. This dataset and the experimental index benchmark are helpful for other scholars to further carry out research related to polarimetric SAR ground terrain classification.

     

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