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All Journal Jurnal Teknik Informatika (JUTIF)
Esti Suryani
Department of Data Science, Universitas Sebelas Maret, Indonesia
Computer Science & IT

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Enhancing Flood Area Segmentation in Remote Sensing Images Using Hybrid Attention Mechanism on DeepLabV3+ with ResNet-50 Backbone Annisa Syifaul Ummah; Esti Suryani; Herdito Ibnu Dewangkoro
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5523

Abstract

Flooding is caused by climate change and urbanization, so rapid and accurate monitoring is essential in supporting emergency response. However, flood segmentation still faces challenges in dense vegetation. This study aims to improve and analyze the performance of the Hybrid Attention Mechanism in the form of Point-wise spatial attention (PSA) and Squeeze-and-Excitation Block (SE Block) in the DeepLabV3+ architecture with the ResNet-50 backbone. The methods used include collecting a dataset of 600 training and 63 validation, data augmentation, model development and Hybrid Attention Mechanism design, hyperparameter optimization, ablation study, and performance evaluation. The ablation results obtained show the best performance with accuracy of 0.9624, F1-score of 0.9618, IoU (Non-Flood) of 0.9323, IoU (Flood) of 0.9208, and mIoU of 0.9265, surpassing previous studies that used Modified U-Net in detecting floods in dense vegetation. This research contributes to the development of a flood segmentation model based on a hybrid attention mechanism, which is more effective in detecting flooded areas in densely vegetated regions.
Co-Authors Annisa Syifaul Ummah Herdito Ibnu Dewangkoro