Search

A new technique for the computation of a characteristic scale for a given image is introduced. Findings reveal that the new technique is robust and stable on different images issued from the remote sensing domain.

Tropospheric inhomogeneities can form a major error source in differential synthetic aperture radar interferometry measurements, which are used in slow-deformation monitoring. Indeed, variations of atmospheric conditions between two radar acquisitions produce variations in the signal path of two images and, thus, additional fringes on differential interferograms. These effects have a strong influence on interferograms and must be compensated to obtain reliable deformation measurements. This paper presents a methodological approach to reduce at both global and local scales tropospheric contributions directly from differential interferograms. It first requires refined knowledge of the stable scatterers that can only be obtained from the analysis of a large population of multitemporal interferograms. The correction of global-scale atmospheric contribution exploits the correlation between phase and topography. The correction of local artifacts is based on the correlation between interferograms containing one common acquisition. This technique is validated on a database of 81 differential interferograms covering the Gulf of Corinth (Greece) and used to improve the measurements of ground deformation compared to global positioning system measurements. Index Terms--Differential synthetic aperture radar interferometry (DinSAR), ground deformation, tropospheric effects. Digital Object Identifier 10.1109/TGRS.2007.894026

The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topagraphy Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital surface model (DSM) over urban areas is the new challenging issue. Since the recent improvements in radar image resolution, synthetic aperture radar (SAR) interferometry, which had already proved its efficiency at low resolution, has provided an accurate tool for urban 3-D monitoring. However, the complexity of urban areas and high-resolution SAR images prevents the straightforward computation of an accurate DSM. In this paper, an original high-level processing chain is proposed to solve this problem, and some results on real data are discussed. The processing chain includes three main steps, namely: 1) information extraction: 2) fusion: and 3) correction. Our main contribution addresses the merging step, where we aim at retrieving both a classification and a DSM while imposing minimal constraint on the building shapes. The joint derivation of height and class enables the introduction of more contextual information. As a consequence, more flexibility toward scene architecture is possible. First, the initial images (interferogram, amplitude, and coherence images) are converted into higher-level information mapping with different approaches (filtering, object recognition, or global classification). Second, these new images are merged into a Markovian framework to jointly retrieve an improved classification and a height map. Third, DSM and classification are improved by computing layover and shadow from the estimated DSM. Comparison between shadow/layover and classification allows some corrections. This paper mainly addresses the second step, while the two others are briefly explained and referred to already published papers. The results obtained on real images are compared to ground truth and indicate a very good accuracy in spite of limited image resolution. The major limit of DSM computation remains the initial spatial and altimetric resolutions that need to be made more precise. Index Terms--Classification, height map, Markovian fusion, synthetic aperture radar (SAR) interferometry, urban areas.

This paper describes a probabilistic method of inferring the position of a point with respect to a reference point knowing their relative spatial position to a third point. We address this problem in the case of incomplete information where only the angular spatial relationships are known. The use of probabilistic representations allows us to model prior knowledge. We derive exact formulae expressing the conditional probability of the position given the two known angles, in typical cases: uniform or Gaussian random prior distributions within rectangular or circular regions. This result is illustrated with respect to two different simulations: The first is devoted to the localization of a mobile phone using only angular relationships, the second, to geopositioning within a city. This last example uses angular relationships and some additional knowledge about the position. Index Terms--Probabilistic geometry, spatial reasoning, geometrical inference.

A new technique for the least-mean-squares (LMS) phase-unwrapping method is developed that incorporates the concept of branch cuts between phase singularities (residues), which are usually associated with the path-following gradient integration technique. These branch cuts are introduced by decomposition of the least-mean-squares unwrapped phase into two separate components. The first results from the transverse part of the wrapped phase gradient, which is induced by residues of the original phase, and the second component is due to a potential component, independent of the residues. This decomposition allows the reconstruction of phase patterns with a high level of accuracy and consistency with the initial (wrapped) phase, even when only partial knowledge of the placement of branch cuts between residues is available. We show how the residue-induced phase, ignored by conventional LMS phase estimators, is reconstructed for a given boundary-value problem. The method is illustrated with interferometric quality-control measurements of optical fiber-connector terminations and also with synthetic aperture radar interferometry. These experiments demonstrate the high accuracy of the method in practical situations in which only a limited number of branch cuts are available.

We propose a methodology to analyze self-similar organization of hydrological basins from satellite imagery and, more specifically, adapted to high-resolution radar images. We show that, when it is applied to several Amazonian rivers and lakes, it provides results in excellent agreement with the fractal theory. This allows us to suggest some novel relations between the river stream length, the river bed area, and the catchment basin area. Fractals, geomorphological measurements, hydrological basin, synthetic aperture radar (SAR).

We 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. We present two local line detectors as well as a method for fusing information from these detectors. In the second global step, we 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. Index Terms - Markov random fields (MRF's), road detection, SAR images, statistical properties.

A new algorithm to estimate the two-dimensional local frequencies of phase interferometric data is described. With a complex sine-wave model, demonstration is given that a conventional multiple-signal classification (MUSIC) algorithm can be used in spite of multiplicative noise perturbations. A faster algorithm dedicated to the processing of interferograms is developed and a measure of confidence in the estimate is proposed. We studied numerical performances using synthetic fringes. As a result of the frequency estimation, knowledge of the fringe local width and orientation can be applied to restore noisy phase data. Results of a complex phase filter are presented for real interferograms obtained from synthetic aperture radar images. Key words: Interferometry, spectral analysis, phase restoration.

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. Index Terms--Bias and errors of the Hough transform, Hough transform, orthogonal directions detection, vanishing points detection.

In this paper, a three-dimensional 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.