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Crop classification based on the combination of polarimetric and temporal features of Sentinel-1 SAR data

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  • Additional Information
    • Publisher Information:
      Institute of Electrical and Electronics Engineers (IEEE) 2024
    • Abstract:
      Polarimetric synthetic aperture radar (PolSAR) can obtain rich information of ground objects through different polarization combinations, and is widely used in terrain classification. The time-varying feature of multi-temporal polarimetric SAR data is a useful supplement, which contains information that is not available in single polarimetric data. This paper aims to introduce time-varying analysis into Sentinel-1 data, so as to effectively combine the information of both time and polarization dimensions. In this way, the accuracy of crop classification using dual-polarimetric data is improved. This paper used Sentinel-1 data, firstly we constructed the dual-polarimetric coherence (DC) based on C 2 matrices, and analyzed the DC to find optimal time by using feature importance ranking in Random Forest model. Then, the polarimetric features and DC of the selected time are combined. Finally, the spatial correlation of MRF was utilized to classify. Compared to using only polarimetric features alone, the overall accuracy is improved.
      This work is supported in part by the National Natural Science Foundation of China under Grant No.62331026.
      Peer Reviewed
      Postprint (author's final draft)
    • Subject Terms:
      Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar; Synthetic aperture radar; Precision farming; Crop classification; Time-varying features; Dual-polarimetric SAR; Random Forest (RF); Markov Random Field (MRF); Radar d'obertura sintètica; Agricultura de precisió; Conference report
    • Availability:
      Open access content. Open access content
      Open Access
    • Note:
      4 p.
      application/pdf
      English
    • Other Numbers:
      HGF oai:upcommons.upc.edu:2117/414960
      Du, Y. [et al.]. Crop classification based on the combination of polarimetric and temporal features of Sentinel-1 SAR data. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2024: 2024 IEEE International Geoscience and Remote Sensing Symposium: 7-12 July, 2024, Athens, Greece: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 4136-4139. ISBN 979-8-3503-6032-5. DOI 10.1109/IGARSS53475.2024.10640752 .
      979-8-3503-6032-5
      10.1109/IGARSS53475.2024.10640752
      1461017959
    • Contributing Source:
      UNIV POLITECNICA DE CATALUNYA
      From OAIster®, provided by the OCLC Cooperative.
    • Accession Number:
      edsoai.on1461017959
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