Enhanced-HisSegNet: Improved SAR Image Flood Segmentation with Learnable Histogram Layers and Active Contour Model

Synthetic Aperture Radar (SAR) imagery plays a vital role in identifying flooded areas in the aftermath causing loss of life and significant economic and environmental damage, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model introduced in [1] through an integration of histogram extraction layers designed for SAR data with Active Contour Models (ACMs), which are integrated into fine-tuned Deep Segmentation Neural Networks (DSNNs). The model was tested on a real SAR dataset, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with histogram layers + ACM, outperforms previous approaches by up to 10% in internal and 4% in external cohorts as intersection over union (IoU) and provides a comprehensive evaluation through metrics like Accuracy and Loss.