Analysis of satellite images for disaster detection

Siti Nor Khuzaimah Binti Amit, Soma Shiraishi, Tetsuo Inoshita, Yoshimitsu Aoki

研究成果: Conference contribution

31 被引用数 (Scopus)

抄録

Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers, followed by max-pooling layers after each convolutional layer, and 2 fully connected layers. We created our own disaster detection training data patches, which is currently focusing on 2 main disasters in Japan and Thailand: landslide and flood. Each disaster's training data set consists of 30000∼40000 patches and all patches are trained automatically in CNN to extract region where disaster occurred instantaneously. The results reveal accuracy of 80%∼90% for both disaster detection. The results presented here may facilitate improvements in detecting natural disaster efficiently by establishing automatic disaster detection system.

本文言語English
ホスト出版物のタイトル2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5189-5192
ページ数4
2016-November
ISBN(電子版)9781509033324
DOI
出版ステータスPublished - 2016 11月 1
イベント36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
継続期間: 2016 7月 102016 7月 15

Other

Other36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
国/地域China
CityBeijing
Period16/7/1016/7/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 地球惑星科学(全般)

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