1887

Abstract

Summary

Environmental monitoring is a very important field nowadays, as it allows for disaster prevention, fire damage assessment, snowmelt forecasts, etc. All this tasks heavy rely on the quality of the satellite data. Radar data is a fairly new approach which allows to retrieve land surface information in a cloudiness condition and even at night. One of the main quality parameters of satellite images is spatial resolution, which stands for how well different small objects differentiate on the image itself. Thus, satellite SAR data spatial resolution enhancement is an important modern task. In this paper a method for synthetic-aperture radar’s (SAR) data spatial resolution enhancement is given, which is based on the fusion of two low-resolution source images into a single one high-resolution image. Radar data retrieved in different polarization modes was converted into unified physical land surface property – dielectric permittivity, using the well-known Oh radar backscattering model. Converted images were fused into a single high-resolution image and the spatial resolution of the result was quantitatively evaluated using the MTF approach.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023510010
2023-10-02
2025-04-19
Loading full text...

Full text loading...

/deliver/fulltext/2214-4609/2023/geoterrace-2023/GeoTerrace-2023-010.html?itemId=/content/papers/10.3997/2214-4609.2023510010&mimeType=html&fmt=ahah

References

  1. Navalgund, R. R., Jayaraman, V., & Roy, P. S. (2007). Remote sensing applications: An overview.Current Science, 93(12), 1747–1766. https://www.jstor.org/stable/24102069
    [Google Scholar]
  2. Kononov, V. I., & Stankevich, S. A. (2004). High and low spatial resolution digital images informativity comparative assessment.Scientific notes of the V.I. Vernadsky Taurida National University, 17(2), 88–95.
    [Google Scholar]
  3. Stankevich, S., Popov, M., Shklyar, S., Sukhanov, K., Andreiev, A., Lysenko, A., Kun, X., Shixiang, C, Yupan, S., Xing, Z., & Boya, S. (2020). Subpixel-shifted Satellite Images Superresolution: Software Implementation.WSEAS Transactions on computers, 19, 31–37. https://doi.org/10.37394/23205.2020.19.5
    [Google Scholar]
  4. Oh, Y. D., KamalSarabandi, & Ulaby, F. T. (1992). An empirical model and an inversion technique for radar scattering from bare soil surfaces.IEEE Transactions on Geoscience and Remote Sensing, 30(2), 370–381. https://doi.org/10.1109/36.134086
    [Google Scholar]
  5. Dubois, P. C, van Zyl, J., & Engman, T. (1995). Measuring soil moisture with imaging radars.IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926. https://doi.org/10.1109/36.406677
    [Google Scholar]
  6. Fung, A. K., Li, Z. B., & Chen, K. F. (1992). Backscattering from a randomly rough dielectric surface. 30(2), 356–369. https://doi.org/10.1109/36.134085
    [Google Scholar]
  7. Stankevich, S. A. (2020). Evaluation of the Spatial Resolution of Digital Aerospace Image by the Bidirectional Point Spread Function Parameterization.Advances in Intelligent Systems and Computing, 317–327. https://doi.org/10.1007/978-3-030-58124-4_31
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023510010
Loading
/content/papers/10.3997/2214-4609.2023510010
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error