Turkmenli, IlterAptoula, ErchanKayabol, Koray2025-01-062025-01-0620241545-598X1558-057110.1109/LGRS.2024.34501222-s2.0-85202721537https://doi.org/10.1109/LGRS.2024.3450122https://hdl.handle.net/20.500.14669/2567Floods are one of the most common natural disasters, causing fatalities and severe economic and environmental impacts, directly affecting agriculture, urban infrastructure, and transportation networks. Hence, it is of utmost importance that flooded areas are efficiently and effectively identified in the aftermath. Synthetic aperture radar (SAR) images are invaluable to this end, since the amount of microwave energy reflected from water is less than that from land, due to its low surface roughness and lack of apparent texture. In this study, we explore the combination of histograms with deep neural networks for the purpose of flood mapping. The proposed histogram extraction layers, specifically designed for SAR content, are integrated into deep segmentation neural networks and are tested on two real SAR datasets. Experimental results have shown that histogram layers integrated into deep segmentation neural networks improve the performance up to 6% in terms of intersection over union (IoU) with a negligible increase in the number of learnable parameters. The code of the work will be available at https://github.com/ilterturkmenli/HistSegNet.eninfo:eu-repo/semantics/closedAccessHistogramsImage segmentationRadar polarimetryFloodsSynthetic aperture radarSentinel-1KernelFlood segmentationhistogram layerSentinel-1 (S1)synthetic aperture radar (SAR)ArticleQ121WOS:001308225500009N/A