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

The synthetic aperture radar (SAR) imagery plays a critical role in flood mapping due to its ability to capture data under all-weather and day-and-night conditions. However, the existing SAR segmentation methods, including the state-of-the-art HisSegNet, face challenges, such as limited generalization, insufficient utilization of SAR-specific features, and suboptimal performance on diverse datasets. To address these limitations, we propose enhanced-HisSegNet, a multimodal fusion strategy that builds upon HisSegNet by integrating learnable histogram layers (HLs) tailored for SAR data with active contour models (ACMs) for precise boundary refinement. These components are embedded into fine-tuned deep segmentation neural networks (DSNNs) to improve segmentation accuracy. Our model was evaluated on real SAR datasets, employing cross-dataset validation for robustness. Experimental results demonstrate significant performance gains, with up to 10% improvement in intersection over union (IoU)—a key metric that measures segmentation accuracy by computing the ratio of intersection to union between the predicted and ground truth regions—on internal datasets and 4% on external datasets, showcasing enhanced accuracy, robustness, and applicability. The code for this work is available at https://github.com/Mohsena1990/Enhanced-HistSegNet.