Change Detection from Areal Imagery Drones Using Siamese U-Net with Spatial Attention Module

Lalu Syamsul Khalid, Grafika Jati, Wahyu Caesarendra, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This research discusses the development of a new model for task change detection. Siamese Neural networks with U-Net as basic architecture are combined with spatial attention modules to perform task change detection. This model is developed to get a lightweight model with good performance. In the implementation, there is no need to use enormous resources. To benchmark the model, we used the LEVIR-CD dataset, where this dataset has two paired images taken at different times. The information contained in the two paired images is that there are changes such as the presence of buildings such as houses that increase or decrease in a certain area during the time of taking the two images. We compared the proposed model with U-Net and Siamese U-Net without spatial attention modules to see how they differ in performance. Then, We also compared the F1 Score with the baseline model of the LEVIR-CD dataset. After hyperparameter tuning with epochs of 100 is performed, the result is that the F1 Scores tested can balance the baseline model with a faster training time.

Original languageEnglish
Title of host publicationIWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-58
Number of pages8
ISBN (Electronic)9781665489508
DOIs
Publication statusPublished - 2022
Event7th International Workshop on Big Data and Information Security, IWBIS 2022 - Depok, Indonesia
Duration: 1 Oct 20223 Oct 2022

Publication series

NameIWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings

Conference

Conference7th International Workshop on Big Data and Information Security, IWBIS 2022
Country/TerritoryIndonesia
CityDepok
Period1/10/223/10/22

Keywords

  • Change Feature
  • Siamese Neural Networks
  • Spatial Attention
  • U-Net

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