Frédéric Galland

Target detection with a liquid crystal-based passive Stokes polarimeter

We present an imaging system that measures the polarimetric state of the light coming from each point of a scene. This system, which determines the four components of the Stokes vector at each spatial location, is based on a liquid-crystal polarization modulator, which makes it possible to acquire four-dimensional Stokes parameter images at a standard video rate. We show that using such polarimetric images instead of simple intensity images can improve target detection and segmentation performance.

Unsupervised Synthetic Aperture Radar Image Segmentation Using Fisher Distributions

A new and fast unsupervised technique for segmentation of high-resolution synthetic aperture radar (SAR) images into homogeneous regions is proposed. This technique is based on Fisher probability density functions (pdfs) of the intensity fluctuations and on an image model that consists of a patchwork of homogeneous regions with polygonal boundaries. The segmentation is obtained by minimizing the stochastic complexity of the image. Different strategies for the pdf parameter estimation are analyzed, and a fast and robust technique is proposed. Finally, the relevance of the proposed approach is demonstrated on high-resolution SAR images.

Synthetic aperture Radar oil spill segmentation by stochastic complexity minimization

We present a new algorithm for oil spill segmentation in synthetic aperture radar (SAR) images, using the minimum description length (MDL) principle and a polygonal active grid. This algorithm is based on two steps: a first partitioning step into homogeneous regions and a second classification step with an automatic MDL thresholding. The obtained method allows one to segment the different candidate oil spills in an image automatically and in a few seconds.

Nonparametric statistical snake based on the minimum stochastic complexity

We propose a nonparametric statistical snake technique that is based on the minimization of the stochastic complexity (minimum description length principle). The probability distributions of the gray levels in the different regions of the image are described with step functions with parameters that are estimated. The segmentation is thus obtained by minimizing a criterion that does not include any parameter to be tuned by the user. We illustrate the robustness of this technique on various types of images with level set and polygonal contour models. The efficiency of this approach is also analyzed in comparison with parametric statistical techniques

Influence of polarization filtering on image registration precision in underwater conditions

Underwater images often suffer from poor visibility due to photon scattering. However, in some cases, optical polarization filtering techniques can decrease the contribution of the scattered light and improve the visual image quality. In this Letter, the influence of these techniques for underwater image registration is analyzed, particularly when backscattered light is the main perturbation induced by the submarine environment. This analysis is performed using the Cramer-Rao bound and relies on a standard image formation model, taking into account various kinds of noises.

Mixed segmentation-detection-based technique for point target detection in nonhomogeneous sky

This paper deals with point target detection in infrared images of the sky for which there are local variations of the gray level mean value. We show that considering a simple image model with the gray level mean value varying as a linear or a quadratic function of the pixel coordinates can improve mixed segmentation-detection performance in comparison to homogeneous model-based approaches.

Minimal Stochastic Complexity Image Partitioning With Unknown Noise Model

We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the gray-level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image. We demonstrate that efficient homogeneous image partitioning can be obtained when no parametric model of the probability laws of the gray levels is used and that this approach leads to a criterion without parameter to be tuned by the user. The efficiency of this technique is compared to a statistical parametric technique on a synthetic image and is compared to a standard unsupervised segmentation method on real optical images

Minimal stochastic complexity snake-based technique adapted to an unknown noise model

We propose a polygonal snake segmentation technique adapted to objects that can be composed of several regions with gray-level fluctuations described by a priori unknown probability laws. This approach is based on a histogram equalization and on the minimization of a criterion without parameter to be tuned by the user. We demonstrate the efficiency of this approach, which has low computational cost, on synthetic and real images perturbed by different types of optical noise.

Comparison of different active polarimetric imaging modes for target detection in outdoor environment.

We address the detection of manufactured objects in different types of environments with active polarimetric imaging. Using an original, fully adaptive imager, we compare several imaging modes having different numbers of polarimetric degrees of freedom. We demonstrate the efficiency of active polarimetric imaging for decamouflage and hazardous object detection, and underline the characteristics that a polarimetric imager aimed at this type of application should possess. We show that in most encountered scenarios the Mueller matrices are nearly diagonal, and sufficient detection performance can be obtained with simple polarimetric imaging systems having reduced degrees of freedom. Moreover, intensity normalization of images is of paramount importance to better reveal polarimetric contrast.

Registering of synthetic aperture radar and optical data

The registration of Synthetic Aperture Radar (SAR) and optical images is a challenging problem, notably in the perspective of the future launch of the Cosmo-SkyMed sensors in the framework of Orfeo program, which will provide high resolution images (below 1 meter). In this paper, we first present the registration of SAR and optical images using a perfect knowledge of the sensor parameters (position, speed, etc, but also the ground height) and we study the influence of a bad knowledge of these parameters. One then shows that feature extraction can be used to perform parameter refining. Finally, one points out that this SAR and optical image registration can be approximated by simple polynomial transformations.