Detection of Flooded Regions from Satellite Images Using Modified UNET - Computational Intelligence in Data Science Access content directly
Conference Papers Year : 2021

Detection of Flooded Regions from Satellite Images Using Modified UNET

S. M. Jaisakthi
  • Function : Author
  • PersonId : 1162769
P. R. Dhanya
  • Function : Author
  • PersonId : 1162770

Abstract

It is necessary to analyze the accessibility of flood-affected regions for better planning of response. Flood level detection using remotely sensed images could minimize costs and can also allow taking adequate preparation for fast recovery. MediaEval 2017 multimedia satellite task dataset is used for flood detection in satellite images. The image segmentation technique on satellite images using the modified UNET convolutional neural network is applied after pre-processing to analyze flood-affected regions and compared them to the corresponding segmented regions during the period of a flood event. Initially, all images were segmented using conventional segmentation methods like the Mean shift clustering algorithm. It is observed that the U-Net model produced a good measure for Intersection over Union (IoU) as 99.46% with 99.41% accuracy. The segmented images are then considered for further conventional processing to get meaningful information of flooded regions from satellite imagery.
Fichier principal
Vignette du fichier
512058_1_En_16_Chapter.pdf (1.25 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03772928 , version 1 (08-09-2022)

Licence

Attribution

Identifiers

Cite

S. M. Jaisakthi, P. R. Dhanya, S. Jitesh Kumar. Detection of Flooded Regions from Satellite Images Using Modified UNET. 4th International Conference on Computational Intelligence in Data Science (ICCIDS), Mar 2021, Chennai, India. pp.167-174, ⟨10.1007/978-3-030-92600-7_16⟩. ⟨hal-03772928⟩
59 View
20 Download

Altmetric

Share

Gmail Facebook X LinkedIn More