Performance Evaluation of 3-D Convolutional Neural Network for Multitemporal Flood Classification Framework With Synthetic Aperture Radar Image Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2025, Vol.18, p.3198-3207
• Main Authors: Sudiana, Dodi, Riyanto, Indra, Rizkinia, Mia, Arief, Rahmat, Prabuwono, Anton Satria, Sumantyo, Josaphat Tetuko Sri, Wikantika, Ketut
• Format: Article
• Language: English
• Subjects: 3-D convolutional neural network (3-D CNN); Accuracy; Artificial neural networks; Cloud cover; Convolutional neural networks; Flood control; Floods; machine learning (ML); multitemporal data; Neural networks; Optical sensors; Performance evaluation; Population studies; Radar; Radar imaging; Radar polarimetry; Rainfall; Remote sensing; SAR (radar); Satellite imagery; Satellite observation; Satellites; Sensors; Synthetic aperture radar; synthetic aperture radar (SAR); temporal variation; Three-dimensional displays; Urban areas; urban flood; Urban populations
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3519523

Urban flooding significantly impacts populations and often coincides with heavy rainfall, making optical satellite observation challenging due to cloud cover. This study proposes a novel approach using synthetic aperture radar (SAR) sensors, which can penetrate clouds, to classify flooded urban areas. The framework employs a 3-D convolutional neural network (3-D CNN) to process multitemporal SAR data from Sentinel-1 (S-1). The dataset included 24 S-1 scenes with Dual VV and VH polarization from March 2019 to February 2020, divided into two co-event images, 18 preevent images, and four postevent images. The 3-D CNN achieved an average overall accuracy of 70.3% and a peak accuracy of 71.8%. These results demonstrate the 3-D CNN's potential to accurately estimate flood extent and identify flood-prone areas, supporting early detection and flood prevention in other cities.