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Combination of Time Series of L-, C- and X-Band SAR Images for Land Cover and Crop Classification

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  • معلومة اضافية
    • Publisher Information:
      IEEE 2022-09-19
    • نبذة مختصرة :
      The availability of new Earth observation satellites operating radar sensors at different frequencies enables the combination of multiple dimensions of the data (time, frequency, polarimetry and interferometry) in many applications. Image classification is expected to benefit from the diversity of observation. This work illustrates classification experiments carried out with series of images acquired by ALOS-2 PALSAR (L-band), Sentinel-1 (C-band) and TanDEM-X (X-band) in two application domains: land cover classification and crop-type mapping. Their usage, both separately and in combination, serves to identify the complementarity of information. In this work we propose a new colour representation of the pair-wise class separability in the case of using three frequency bands, which help identify which bands (or combinations of them) provide the best performance. Results in terms of accuracy scores (overall and class-specific) show that the use of the three frequency bands always outperforms the individual bands and their pairs. In addition, for both land classification and crop-type mapping the accuracy of using coherence time series is lower than the one obtained with the intensity time series, but there is complementarity in terms of sensitivity when both coherence and intensity time series are used together. The classes which are most benefited at each particular case of study have been identified. Finally, a partial trade-off has been found between the use of multiple frequency bands and the length of the available time series.
    • الموضوع:
      Synthetic Aperture Radar (SAR); Land cover classification; Crop classification; Interferometry; Time series; info:eu-repo/semantics/article
    • Other Numbers:
      ESALI oai:rua.ua.es:10045/127505
      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15: 8266-8286. https://doi.org/10.1109/JSTARS.2022.3207574
      1939-1404 (Print)
      2151-1535 (Online)
      10.1109/JSTARS.2022.3207574
      1364651290
    • Contributing Source:
      UNIV DE ALICANTE
      From OAIster®, provided by the OCLC Cooperative.
    • الرقم المعرف:
      edsoai.on1364651290
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