Georges Stamon

2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives

The analysis of remotely sensed data for object extraction is a key step in an increasing number of GIS (Geographic Information Science) applications, in particular for mapping, updating and change detection purposes. The main goal of this paper is to present an automatic method for detecting changes in a 2D building database, starting from recent satellite images. The workflow of our method is divided into two steps. 3D primitives, extracted from multiple images or from a correlation Digital Surface Model (DSM), are firstly collected for each building and matched with primitives derived from the existing database in order to achieve a final decision about acceptance or rejection. A specific algorithm, based on the DSM and a computed Digital Terrain Model (DTM), is subsequently used to extract new buildings. The method is here introduced and tested in two test areas, very different regarding the land use and topography. The outcomes of the method are assessed and show the good performance of our system, especially in terms of completeness, robustness and transferability.

Morphological mesh filtering and α-objects

Since image analysis techniques have come to maturity, mesh analysis has remained challenging requiring more and more efforts for elaborating an effective theoretical model. In this article, following algebraic mesh operators, we introduce algorithms that perform morphological transformations on unorganized point sets connected by their Delaunay triangulations. We show that these algorithms correspond to morphological operators like erosion, dilation or opening, acting as shape filters on meshes. More theoretically, a link is established between these algorithms and the formalisms of edge algebra and @a-objects. Then, the mesh operators are defined in terms of complete lattices. These algorithms are applied to the problem - among others - of scene reconstruction by stereoscopy in which objects are represented by unstructured and noisy clouds of 3D points. Rapid prototyping should also benefit from these algorithms.

Morphological operations on Delaunay triangulations

Algorithms are presented which allow us to perform morphological transformations on unorganised sets of points represented by their Delaunay triangulation. The results show that these algorithms could behave as morphological operators such as erosion, dilatation, and opening do. They actually act as shape filters. These algorithms are applied to the problem of scene reconstruction by stereoscopy in which objects are represented by unstructured clouds of 3D points.

Automatic Revision of 2D Building Databases from High Resolution Satellite Imagery: A 3D Photogrammetric Approach

Among all the issues involved in Geographic Information Science, automating the update of 2D building databases is a crucial and challenging issue. Such an update usually starts out with a manual change detection process. The main goal of this paper is to present a new method to automate the detection of changes in a 2D building database, starting from satellite images. The workflow of our approach is divided into 2 phases. Primitives, extracted from multiple images or from a correlation Digital Surface Model (DSM), are firstly collected for each building and matched with primitives derived from the existing database to achieve a final decision about acceptance or rejection. A specific algorithm, based on the DSM and a computed Digital Terrain Model (DTM), is subsequently used to extract new buildings. The method is here introduced and tested in two test areas, very different regarding the land use and topography. The outcomes of the method are assessed and show the good performance of our system, especially in terms of completeness, robustness and transferability.

Chapter 4 - Point Set Analysis

Abstract Dealing with imaging issues usually entails handling digital radiometric images. However, visual data can be efficiently handled as geometric point sets either due to the nature of the acquisition device or the intrinsic redundancy within large amounts of radiometric data. Most research works about geometric structures are related to computer graphics and image synthesis; meshes as graph representations have been involved only in a few image analysis issues to date. Yet, much room remains for completing the visual analysis tools as most image analysis algorithms are designed ro radiometric data distributed over a regular grid. We propose to extend the standard image analysis toolbox to unstructured point sets usually connected via mesh structures such as Delaunay triangulations. A particular focus on mathematical morphology sheds light on the potential applications of these ideas. More specifically, applications to digital microscopy imaging issues are discussed and preliminary results are presented.

Flash radar geometric simulator

The automatic processing of radar images (pattern recognition, segmentation, data fusion) is restricted by the presence of geometric distortions and radiometric variability induced by unrectified relief and erroneous parameters. This is especially true in the case of a flash radar where the reflectivity map is built by merging strip images, each one with its proper geometric errors. The conception of a fast radar simulator using the flash radar geometric relationship would allow to study the geometric errors and to define the best viewing parameters.

2D change detection from satellite imagery: Performance analysis and impact of the spatial resolution of input images

In the past few years, many change detection methods have been proposed in the GIS community to support the update of existing building databases. If most of these methods use images to settle the problem, only a few of them analyse the optimal resolution to use for input images. The main goal of this paper - that focuses on the use of satellite images - is to provide possible answers to this question. For that purpose, a change detection method is tested on several test areas, for which input images are available at different resolutions (50 cm and 70 cm). The outcomes of the method are then evaluated and analysed in order to highlight the impact of the spatial resolution of input images on the systems efficiency.

Automatic estimation of fine terrain models from multiple high-resolution satellite images

In this paper, we present an automatic method for deriving Digital Terrain Models (DTM) from Digital Surface Models (DSM). The DTM generation is formalized here as the minimization of a given energy, defined by a data term and a regularization term. The main contribution of this paper lies in the specific module, introduced in the data term in order to filter outliers (non ground points present in the original DSM) so that the final terrain surface best fits true ground points (inliers). The method is here introduced and tested on a vegetated and hilly area. A comparison to ground truth is also performed and shows the geometric accuracy and the robustness of the approach.

Geometric ortho-rectification of flash radar images

The cartographic use of radar images and multisensor images fusion requires rectified images. The geometric errors of a radar image are induced by flight and system errors, as well as terrain elevation. The flash radar is a fast processing radar that acquires an image strip by strip, with a position dependent resolution. The ortho-rectification process must then deal with two different problems: correct each strip, and merge them. Due to the small number of available ground control points, and overlapping areas between strips, these two steps must be processed together. The paper presents two types of specific methods to correct flash radar images: interpolative methods, based on polynomial least square fitting, and parametric methods, based on nonlinear least square adjustment of a mathematical model of the flash radar geometric relations. A comparison is made between the first results obtained with these methods.<<ETX>>

A method for discontinuous neurite reconstruction based on diffusion tensor, Hessian eigenvector, and diffused gradient vector fields

An a posteriori tracing method for broken neurite reconstruction is proposed. It uses local line structure cues from three vector fields; diffusion tensor vector field, Hessian eigenvector field, and diffused gradient vector field. The vector fields are built to give directionalities of line structures with gaps in different configurations. The method uses an iterative search and extend approach to reconstruct possible links between broken neurite segments which for various reasons existing tracing methods fail to extract. Iterative nature of the method enables us to extend traced neurites in directions aligned with underlying line structures. Subsequently, the method connects traced neurites across gaps in presence of other traced neurite segments or disposes extensions otherwise.