Share:


Influence of Noise Equivalent Beta Naught estimation on backscattering image classification of TerraSAR-X

    Sumaya Falih Hasan Affiliation
    ; Muntadher Aidi Shareef Affiliation
    ; Hussein Sabah Jaber Affiliation

Abstract

Noise Equivalent Beta Naught is the different noise influence that beneficence to the radar signal. This type of noise is available in TerraSAR-X satellite images and expressed in forms of scaled polynomial described the noise power. On the other hand, Sigma naught or backscattering coefficient represents the average reflectivity of a horizontal material samples which used to reflect the nature of the land use and land cover in radar images. In this paper, radar satellite images in dual VV and HH polarization were used to study the influence of the noise on backscattering image classification. The result demonstrated that the visual interpretation of sigma naught which is result from the comparison between existence case and absence case (in the other word: with and without noise) of the noise illustrated that there is no different between them. In the other hand, for more details and more precise, an example of small images are used to show the values of obtained backscattering. The result demonstrated that the NEBN plays the main roles in decreasing the values of backscattering coefficient in TSX image. The influence of this noise had usually high in water body surface, because this surface is generally having small backscattering coefficients compared with land cover.

Keyword : Noise Equivalent Beta Naught, TerraSAR-X, Strip map image, dual polarization

How to Cite
Hasan, S. F., Shareef, M. A., & Jaber, H. S. (2024). Influence of Noise Equivalent Beta Naught estimation on backscattering image classification of TerraSAR-X. Geodesy and Cartography, 50(2), 104–112. https://doi.org/10.3846/gac.2024.18264
Published in Issue
Sep 25, 2024
Abstract Views
91
PDF Downloads
74
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Balss, U., Breit, H., & Fritz, T. (2009). Noise-related radiometric correction in the TerraSAR-X multimode SAR processor. IEEE Transactions on Geoscience and Remote Sensing, 48(2), 741–750. https://doi.org/10.1109/TGRS.2009.2035443

Breit, H., Fritz, T., Balss, U., Lachaise, M., Niedermeier, A., & Vonavka, M. (2009). TerraSAR-X SAR processing and products. IEEE Transactions on Geoscience and Remote Sensing, 48(2), 727–740. https://doi.org/10.1109/TGRS.2009.2035497

Buckreuss, S., Schättler, B., Fritz, T., Mittermayer, J., Kahle, R., Maurer, E., Böer, J., Bachmann, M., Mrowka, F., Schwarz, E., Breit, H., & Steinbrecher, U. (2018). Ten years of TerraSAR-X operations. Remote Sensing, 10(6), Article 873. https://doi.org/10.3390/rs10060873

Buckreuss, S., Werninghaus, R., & Pitz, W. (2008). The German satellite mission TerraSAR-X. In 2008 IEEE Radar Conference (pp. 1–5). IEEE. https://doi.org/10.1109/RADAR.2008.4720788

Dibs, H., Hasab, H. A., Jaber, H. S., & Al-Ansari, N. (2022). Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images. Innovative Infrastructure Solutions, 7(1), 1–14. https://doi.org/10.1007/s41062-021-00598-7

Gregorek, D., Günzel, D., Rust, J., Paul, S., Velotto, D., Imber, J., Tings, B., & Frost, A. (2019). FPGA prototyping of a high-resolution TerraSAR-X image processor for iceberg detection. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers (pp. 1832–1835). IEEE. https://doi.org/10.1109/IEEECONF44664.2019.9048985

Hasan, S. F., Shareef, M. A., & Hassan, N. D. (2021). Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq). Arabian Journal of Geosiences, 14(4), Ar­ticle 276. https://doi.org/10.1007/s12517-021-06494-9

Jaber, H. S., Shareef, M. A., & Merzah, Z. F. (2022). Object-based approaches for land use-land cover classification using high resolution quick bird satellite imagery (a case study: Kerbela, Iraq). Geodesy and Cartography, 48(2), 85–91. https://doi.org/10.3846/gac.2022.14453

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456

Maarez, H. G., Jaber, H. S., & Shareef, M. A. (2022). Utilization of Geographic Information System for hydrological analyses: A case study of Karbala province, Iraq. Iraqi Journal of Science, 63(9), 4118–4130. https://doi.org/10.24996/ijs.2022.63.9.39

Mittermayer, J., Wollstadt, S., Prats-Iraola, P., & Scheiber, R. (2013). The TerraSAR-X staring spotlight mode concept. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3695–3706. https://doi.org/10.1109/TGRS.2013.2274821

Mohammed Noori, A., Falih Hasan, S., Mahmood Ajaj, Q., Ridha Mezaal, M., Z. M. Shafri, H., & Aidi Shareef, M. (2018). Fusion of airborne hyperspectral and WorldView2 multispectral images for detailed urban land cover classification a case study of Kuala Lumpur, Malaysia. International Journal of Engineering & Technology, 7(4.37), 202–206. https://doi.org/10.14419/ijet.v7i4.37.24102

Moreira, A., Zink, M., Bartusch, M., Quiroz, A. E. N., & Stettner, S. (2021). German spaceborne SAR missions. In 2021 IEEE Radar Conference (RadarConf21) (pp. 1–6). IEEE. https://doi.org/10.1109/RadarConf2147009.2021.9455326

Naught, B., & Naught, S. (2014). Radiometric calibration of TerraSAR-X data. Airbus Defence & Space.

Rasti, B., Chang, Y., Dalsasso, E., Denis, L., & Ghamisi, P. (2022). Image restoration for remote sensing: Overview and toolbox. IEEE Geoscience and Remote Sensing Magazine, 10(2), 201–230. https://doi.org/10.1109/MGRS.2021.3121761

Schwerdt, M., Brautigam, B., Bachmann, M., & Doring, B. (2007). TerraSAR-X calibration-first results. In 2007 IEEE International Geoscience and Remote Sensing Symposium (pp. 3932–3935). IEEE. https://doi.org/10.1109/IGARSS.2007.4423705

Schwerdt, M., Bräutigam, B., Bachmann, M., & Döring, B. (2008). TerraSAR-X calibration results. In 7th European Conference on Synthetic Aperture Radar (pp. 1–4). VDE. https://doi.org/10.1109/IGARSS.2008.4778963

Shareef, M. A., Toumi, A., & Khenchaf, A. (2015). Estimation and characterization of physical and inorganic chemical indicators of water quality by using SAR images. In Proceedings of SPIE, SAR Image Analysis, Modeling, and Techniques XV (Vol. 9642, pp. 140–150). SPIE. https://doi.org/10.1117/12.2194503

Yang, D., & Jeong, H. (2018). Verification of Kompsat-5 sigma naught equation. Korean Journal of Remote Sensing, 34(6_3), 1457–1468.