Authors:

I Made Oka Guna Antara, 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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PDF:

https://jurnal.harianregional.com/ecotrophic/full-53996

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 Aug. 2025. doi:https://doi.org/10.24843/EJES.2019.v13.i02.p09.

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ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 13 No 2 (2019)

Section

Articles

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