Target detection and segmentation in coherent active polarimetric images

We address the problem of target segmentation and detection in coherent active polarimetric images. We consider polarimetric imagers which illuminate the scene with a laser beam and which measure how this light is depolarized by the scene. Since they are formed with coherent light, these "depolarization" images are perturbed by strong speckle-like noise, and by the spatial nonuniformity of the illumination beam. We design image processing algorithms which are adapted to the original statistical properties of these images. We address object segmentation using statistical active contours and target detection using maximum-likelihood (ML) algorithms. We show that the proposed algorithms perform well on both simulated and real-world images