Analysis of satellite images for disaster detection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

43 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781509033324
Publication statusPublished - 2016 Nov 1
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: 2016 Jul 102016 Jul 15


Other36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016


  • convolutional neural network
  • difference extraction
  • disaster detection
  • satellite images

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)


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