Hanbay, Kazim2026-04-042026-04-0420251070-99081558-2361https://doi.org/10.1109/LSP.2025.3571640https://hdl.handle.net/11616/109129Synthetic Aperture Radar (SAR) images are frequently used because they provide optimal image quality in all weather conditions. Nevertheless, SAR ship detection has two difficulties. One is coherent speckle noise, which raises false alarms and confuses ships with similar objects. This letter proposes an efficient ship detector for low contrast, inshore and dense targets. First, to accurately eliminate the land areas and speckle noise, the hessian matrix and eigenvalues of the images were calculated. The largest eigenvalue information was given as input to the Gaussian function and the standard deviation and average images of the images were calculated. Then, the standard deviation and average images were combined with a probabilistic approach to obtain an image that highlights the ship regions. Morphological operations and connected component analysis were performed on this image. Experimental results showed that the proposed method provides both accurate and faster results.eninfo:eu-repo/semantics/closedAccessMarine vehiclesRadar polarimetryEigenvalues and eigenfunctionsStandardsSynthetic aperture radarMeteorologyDeep learningCoastlinesAccuracyTrainingEigenvaluesGaussian probabilityship detectionsynthetic aperture radar (SAR)Article322214221810.1109/LSP.2025.35716402-s2.0-105005883337Q1WOS:001499502100003Q20000-0003-1374-1417