Mathias Ortner

Building Outline Extraction from Digital Elevation Models Using Marked Point Processes

This work presents an automatic algorithm for extracting vectorial land registers from altimetric data in dense urban areas. We focus on elementary shape extraction and propose a method that extracts rectangular buildings. The result is a vectorial land register that can be used, for instance, to perform precise roof shape estimation. Using a spatial point process framework, we model towns as configurations of and unknown number of rectangles. An energy is defined, which takes into account both low level information provided by the altimetry of the scene, and geometric knowledge about the disposition of buildings in towns. Estimation is done by minimizing the energy using simulated annealing. We use an MCMC sampler that is a combination of general Metropolis Hastings Green techniques and the Geyer and Møller algorithm for point process sampling. We define some original proposition kernels, such as birth or death in a neighborhood and define the energy with respect to an inhomogeneous Poisson point process. We present results on real data provided by the IGN (French National Geographic Institute). Results were obtained automatically. These results consist of configurations of rectangles describing a dense urban area.

A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models

This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a priori knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, whereas the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, and a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of digital elevation models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the French National Geographic Institute (IGN) consisting in low-quality DEMs of various types.

Estimation of an Observation Satellite’s Attitude Using Multimodal Pushbroom Cameras

Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant overtime. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellites attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets.

Estimating satellite attitude from pushbroom sensors

Linear pushbroom cameras are widely used in passive remote sensing from space as they provide high resolution images. In earth observation applications, where several pushbroom sensors are mounted in a single focal plane, small dynamic disturbances of the satellites orientation lead to noticeable geometrical distortions in the images. In this paper, we present a global method to estimate those disturbances, which are effectively vibrations. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. To do so, we embed the estimation process in a Bayesian framework. An autoregressive model is used as a prior on the vibrations. The problem can be seen as a global image registration task where multiple pushbroom images are registered to the same coordinate system, the registration parameters being the vibration coefficients. An alternating maximisation procedure is designed to obtain Maximum a Posteriori estimates (MAP) of the vibrations as well as of the autoregressive model coefficients. We illustrate the performance of our algorithm on various datasets of satellite imagery1.

Point Processes of Segments and Rectangles for Building Extraction from Digital Elevation Models

In this work, we propose a new model based on stochastic geometry for extracting features from images. This type of model allows the incorporation of a prior knowledge on the interactions between features within the extraction process. We focus on the specific problem of automatic building extraction from digital elevation models (DEMs). The model we propose is based on two interacting spatial point processes, the former being a process of rectangles, the latter a process of segments. An energy associated with the resulting process is defined. This energy consists in five main parts. We first define two energy data terms to make the rectangles fit the homogeneous areas and the segments fit meaningful discontinuities. Two prior terms favoring respectively the alignment of rectangles and the connection of segments are incorporated. The last part of the energy is an interaction term that makes the two types of objects cooperate. We present results on real data provided by the IGN (French Geographic Institute)

Satellite image registration for attitude estimation with a constrained polynomial model

Satellite image registration has been investigated for several years. Nevertheless, little attention has been paid to the linear geometry of the satellites imaging sensor, often consisting of several pushbroom cameras. Each pushbroom camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. In this paper, we expose the problem and present a constrained image registration method to estimate the satellites attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework and a piecewise polynomial model under constraints to deduce the registration equation. The performances of our algorithm are shown on two satellite datasets.

A Reversible Jump MCMC Sampler for Object Detection in Image Processing

To detect an unknown number of objects from high resolution images, we use spatial point processes models. The method is adapted to our image processing applications since it describes images as realizations of a point process whose points represent geometrical objects. We consider models made of two parts: a data term which quantifies the relevance of a set of objects with respect to the image and a prior term, containing strong geometrical interactions between objects. We use the Maximum A Posteriori estimator, which is obtained by combining a reversible Markov chain monte carlo (RJMCMC) point process sampler with a simulated annealing procedure. The quality of the results and the speed of the algorithm strongly depend on the used sampler. We present here an adaptation of Geyer-Moller sampler for point processes and show that the resulting Markov Chain keeps the required convergence properties. In particular, we design an updating scheme which allows the generation of points in the neighborhood of some others, and check the relevance of such moves on a toy example. We present experimental results on the difficult problem of the detection of buildings in a Digital Elevation Model of a dense urban area.

IMage-based satellite attitude estimation

Pushbroom sensors are widely used for earth observation from space; this camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. Current solutions use inertial sensors to record attitude variations and correct images, but they are expensive and not accurate enough for high frequency variations. In this paper, we expose the problem and present an image registration method to estimate the satellites attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework to deduce the registration equation; the performances of our algorithm are shown on two satellite datasets.

Sensor measurements and image registration fusion to retrieve variations of satellite attitude

Observation satellites use pushbroom sensors to capture images of the earth. These linear cameras acquire 1-D images over time and use the straight motion of the satellite to sweep out a region of space and build 2-D images. The stability of the imaging platform is crucial during the acquisition process to guaranty distortion free images. Positioning sensors are used to control and rectify the attitude variations of the satellite, but their sampling rate is too low to provide an accurate estimate of the motion. In this paper, we describe a way to fuse star tracker measurements with image registration in order to retrieve the attitude variations of the satellite. We introduce first a simplified motion model where the pushbroom camera is rotating during the acquisition of an image. Then we present the fusion model which combines low and high frequency informations of respectively the star tracker and the images; this is embedded in a Bayesian setting. Lastly, we illustrate the performance of our algorithm on three satellite datasets.