Henri Maître

Detection of linear features in SAR images: application to road network extraction

The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. The authors present two local line detectors as well as a method for fusing information from these detectors. In the second global step, they identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.

Fuzzy mathematical morphologies: A comparative study

Fuzzy set theory has found a promising field of application in the domain of digital image processing, since fuzziness is an intrinsic property of images. For dealing with spatial information in this framework from the signal level to the highest decision level, several attempts have been made to define mathematical morphology on fuzzy sets. The purpose of this paper is to present and discuss the different ways to build a fuzzy mathematical morphology. We will compare their properties with respect to mathematical morphology and to fuzzy sets and interpret them in terms of logic and decision theory.

A new statistical model for Markovian classification of urban areas in high-resolution SAR images

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.

Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation

In this letter, we are interested in the annotation of large satellite images, using semantic concepts defined by the user. This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches. Given a training set of images for each concept, learning is based on the latent Dirichlet allocation (LDA) model. This hierarchical model represents each item of a collection as a random mixture of latent topics, where each topic is characterized by a distribution over words. The LDA-based image representation is obtained using simple features extracted from image words. We then exploit the capability of the LDA model to assign probabilities to unseen images, in order to classify the patches of the large image into the semantic concepts, using the maximum-likelihood method. We conduct experiments on panchromatic QuickBird images with 60-cm resolution. Taking into account the spatial information between the patches shows to improve the annotation performance.

Contribution to the determination of vanishing points using Hough transform

We propose a method to locate three vanishing points on an image, corresponding to three orthogonal directions of the scene. This method is based on two cascaded Hough transforms. We show that, even in the case of synthetic images of high quality, a naive approach may fail, essentially because of the errors due to the limitation of the image size. We take into account these errors as well as errors due to detection inaccuracy of the image segments, and provide a method efficient, even in the case of real complex scenes. >

Strict integrity control of biomedical images

The control of the integrity and authentication of medical images is becoming ever more important within the Medical Information Systems (MIS). The intra- and interhospital exchange of images, such as in the PACS (Picture Archiving and Communication Systems), and the ease of copying, manipulation and distribution of images have brought forth the security aspects. In this paper we focus on the role of watermarking for MIS security and address the problem of integrity control of medical images. We discuss alternative schemes to extract verification signatures and compare their tamper detection performance.

A 3D reconstruction of vascular structures from two X-ray angiograms using an adapted simulated annealing algorithm

A three-dimensional (3D) reconstruction of the vessel lumen from two angiographic views, based on the reconstruction of a series of cross-sections, is proposed. Assuming uniform mixing of contrast medium and background subtraction, the cross-section of each vessel is reconstructed through a binary representation. A priori information about both the slice to be reconstructed and the relationships between adjacent slices are incorporated to lessen ambiguities on the reconstruction. Taking into account the knowledge of normal vessel geometry, an initial solution of each slice is created using an elliptic model-based method. This initial solution is then deformed to be made consistent with projection data while being constrained into a connected realistic shape. For that purpose, properties on the expected optimal solution are described through a Markov random field. To find an optimal solution, a specific optimization algorithm based on simulated annealing is used. The method performs well both on single vessels and on branching vessels possessing an additional inherent ambiguity when viewed at oblique angles. Results on 2D slice independent reconstruction and 3D reconstruction of a stack of spatially continuous 2D slices are presented for single vessels and bifurcations.

3-D Reconstruction of Urban Scenes from Aerial Stereo Imagery

A contribution to the automatic 3-D reconstruction of complex urban scenes from aerial stereo pairs is proposed. It consists of segmenting the scene into two different kinds of components: the ground and the above-ground objects. The above-ground objects are classified either as buildings or as vegetation. The idea is to define appropriate regions of interest in order to achieve a relevant 3-D reconstruction. For that purpose, a digital elevation model of the scene is first computed and segmented into above-ground regions using a Markov random field model. Then a radiometric analysis is used to classify above-ground regions as building or vegetation, leading to the determination of the final above-ground objects. The originality of the method is its ability to cope with extended above-ground areas, even in case of a sloping ground surface. This characteristic is necessary in a urban environment. Results are very robust to image and scene variability, and they enable the utilization of appropriate local 3-D reconstruction algorithms.

Improving phase unwrapping techniques by the use of local frequency estimates

In multipass spaceborne synthetic aperture radar (SAR) interferometry, the two acquisitions often present low correlation levels and very noisy phase measurements that are incompatible with automatic phase unwrapping. Instead of dealing with many residues due to erroneous-wrapped phase differences, the authors propose to use the local frequency as measured by a spectral analysis algorithm presented in a previous paper, E. Trouve et al. (1996). For this purpose, the authors present two conventional unwrapping algorithms, one local and the other global, which they revisit to benefit from the robust focal frequency estimates. For a local approach based on path-following techniques, they use the frequency estimates in a slope-compensated filter that extend the complex averaging up to a sufficient number of looks to eliminate residues due to the noise. Then they connect residues due to noninterferometric features along mask components resulting from the detection of layovers and uncorrelated areas. For a global approach, such as the weighted least-squares methods, they demonstrate that the use of noisy discrete phase gradient leads to a biased solution. To avoid this drawback, they propose to use the local frequency estimate and associated measure of confidence as phase gradient and weight. Results are presented on both topographic and differential interferograms obtained from the ERS-1 European radar satellite over various landscapes and the displacement field of the Landers 1992 earthquake.

A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors

The authors propose a method aiming to characterize the spatial organization of the main cartographic elements of a synthetic aperture radar (SAR) image and thus giving an almost automatic interpretation of the scene. Their approach is divided into three main steps which build the whole image interpretation gradually. The first step consists of applying low-level detectors taking the speckle statistics into account and extracting some raw information from the scene. The detector responses are then fused in a second step using Dempster-Shafer theory, thus allowing the modeling of the knowledge that there is about operators, including possible ignorance and their limits. A third step gives the final image interpretation using contextual knowledge between the different classes. Results of the whole method applied to different SAR images and to various landscapes are presented.