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.
Highlights
The study of landslide location is in Hokkaido, Japan, which occurred due to the Iburi Earthquake 2018
In this study an analysis was carried out using satellite images of ALOS-2/PALSAR-2
SegNet is a deep learning architecture, the function is for Semantic Segmentation as a labeler for each pixel image according to the class of the object that has been determined (Garcia-Garcia et al, 2018)
Summary
The study of landslide location is in Hokkaido, Japan, which occurred due to the Iburi Earthquake 2018. ALOS-2/PALSAR-2 is a satellite image of Synthetic Aperture Radar (SAR). ECOTROPHIC VOLUME 13 NOMOR 2 TAHUN 2019 p-ISSN:1907-5626,e-ISSN:2503-3395 magnitude and phase on four polarizations (HH, HV, VH, VV) sent and received horizontally (H) and vertically (V) by the radar antenna.Various methods have been developed previously to obtain information on the surface characteristics of the earth from Fully Pol-SAR data. We try to apply Deep Learning with SegNet architecture to the fourcomponent Yamaguchi’s decomposition. One of the popular algorithms for deep learning in images and videos is Convolutional Neural Networks (CNN). SegNet is a deep learning architecture, the function is for Semantic Segmentation as a labeler for each pixel image according to the class of the object that has been determined (Garcia-Garcia et al, 2018). We want to adopt this architecture for this research because of its advantages
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