A TEMPORAL DEEP CONVOLUTIONAL NEURAL NETWORK MODEL ON SENTINEL-1 IMAGE TIME SERIES FOR PIXEL-WISE FLOOD CLASSIFICATION

Accurate and timely flood mapping is important in emergency management which can be greatly served by Synthetic Aperture Radar (SAR). Research on SAR flood detection is mostly based on thresholding that has low time complexity and seems ideal for emergency response, although human intervention is needed. Machine learning methods have fewer errors and minimize human intervention but their computational complexity is higher. This work aims to provide a lightweight convolutional neural network baseline for pixelwise time series flood classification in open land on SAR satellite data. Quantitative and qualitative evaluation of results indicate that the approach is promising.