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Polarimetric imaging data contains rich information about surface textures, shape, and shading of objects in a scene, which can be used to discriminate objects from background. Due to spectral signatures are limited to material properties, separating man-made objects from natural scene is a difficult task in complex scene. In this paper, we present a man-made object separation technique from natural scene utilizing polarimetric data. We started by calculating different polarimetric component images based on Stokes vector measurement. After that, combining polarimetric component images into color space and single-channel conversion, a polarimetric signature image is calculated. Then, considering pixel neighborhood relationship, an incremental clustering approach is applied to group similar pixel patterns of polarimetric signature image. Finally, a morphological structuring element is applied to conduct the morphological close operation to refine the final background mask. Ground truth is generated manually. A high-performing score (Dice Similarity Coefficient (DSC)) is achieved on the final man-made object area mask which separates man-made objects well from natural scene. Future work will exploit the use of multispectral polarimetric imagery for target classification using machine learning techniques.
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