AN APPLICATION OF SEGNET FOR DETECTING LANDSLIDE AREAS BY USING FULLY POLARIMETRIC SAR DATA

  • I Made Oka Guna Antara Graduate Student of Environmental Science, Udayana University
  • Norikazu Shimizu
  • Takahiro Osawa
  • I Wayan Nuarsa

Abstract

The study location of landslide is in Hokkaido, Japan which occurred due to the Iburi Earthquake 2018. In this study the landslide has been estimated by the fully Polarimetric SAR (Pol-SAR) technique based on ALOS-2 PALSAR-2 data using the Yamaguchi’s decomposition. The Yamaguchi's decomposition is proposed by Yoshio Yamaguchi et.al. The data has been analyzed using the deep learning process with SegNet architecture with color composite. In this research, the performance of SegNet is fast and efficient in memory usage. However, the result is not good, based on the Intersection over Union (IoU) evaluation obtained the lowest value is 0.0515 and the highest value is 0.1483. That is because of difficulty to make training datasets and of a small number of datasets. The greater difference between accuracy and loss graph along with higher epochs represents overfitting. The overfitting can be caused by the limited amount of training data and failure of the network to generalize the feature set over the training images.

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Published
2019-11-30
How to Cite
ANTARA, I Made Oka Guna et al. AN APPLICATION OF SEGNET FOR DETECTING LANDSLIDE AREAS BY USING FULLY POLARIMETRIC SAR DATA. ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science), [S.l.], v. 13, n. 2, p. 215-226, nov. 2019. ISSN 2503-3395. Available at: <https://ojs.unud.ac.id/index.php/ECOTROPHIC/article/view/53996>. Date accessed: 28 sep. 2022. doi: https://doi.org/10.24843/EJES.2019.v13.i02.p09.
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Articles