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

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

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 area...

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Bibliographic Details
Main Authors: Dodi Sudiana, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo, Ketut Wikantika
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
3-D convolutional neural network (3-D CNN)
machine learning (ML)
multitemporal data
synthetic aperture radar (SAR)
temporal variation
urban flood
Online Access:https://ieeexplore.ieee.org/document/10805564/
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https://ieeexplore.ieee.org/document/10805564/

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