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Roger Fjortoft

Bio: Roger Fjortoft is an academic researcher from Centre National D'Etudes Spatiales. The author has contributed to research in topics: Synthetic aperture radar & Radar imaging. The author has an hindex of 8, co-authored 28 publications receiving 717 citations.

Papers
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Journal ArticleDOI
TL;DR: The polarization uniqueness in transmission of this mixed basis mode, hereafter referred to as the /spl pi//4 mode, maintains the standard lower pulse repetition frequency operation and hence maximizes the coverage of the sensor.
Abstract: We assess the performance of synthetic aperture radar (SAR) compact polarimetry architectures based on mixed basis measurements, where the transmitter polarization is either circular or orientated at 45/spl deg/(/spl pi//4), and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight. An original algorithm is proposed to reconstruct the full polarimetric (FP) information from this architecture. The performance assessment is twofold: it first concerns the level of information preserved in comparison with FP, both for point target analysis and crop fields classification, using L-band SIRC/XSAR images acquired over Landes forest and Jet Propulsion Laboratory AIRSAR images acquired over Flevoland. Then, it addresses the space implementation complexity, in terms of processed swath, downloading features, power budget, calibration, and ionospheric effects. The polarization uniqueness in transmission of this mixed basis mode, hereafter referred to as the /spl pi//4 mode, maintains the standard lower pulse repetition frequency operation and hence maximizes the coverage of the sensor. Because of the mismatch between transmitter and receiver basis, the power budget is deteriorated by a factor of 3 dB, but it can partly be compensated.

322 citations

Journal ArticleDOI
TL;DR: The two approaches to unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation are compared and it is shown that they can be combined in a way that conserves their respective advantages.
Abstract: Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented.

155 citations

Journal ArticleDOI
TL;DR: The experimental results confirm expected characteristics of near-nadir Ka-band interferometric SAR imagery, such as strong water/land radiometric contrast and very highInterferometric coherence on water.
Abstract: The principal instrument of the NASA/CNES wide-swath altimetry mission Surface Water and Ocean Topography (SWOT) is the Ka-band Radar Interferometer (KaRIn), a bistatic synthetic aperture radar (SAR) system operating on near-nadir swaths on both sides of the satellite track. There are limited reports on backscattering from natural surfaces at this short wavelength and particular observation geometry. Near-field backscattering measurements on water, as well as the first interferometric airborne SAR acquisitions at Ka-band covering the 0.6 °-3.9 ° incidence range of KaRIn, were therefore conducted. The experimental results confirm expected characteristics of near-nadir Ka-band interferometric SAR imagery, such as strong water/land radiometric contrast (typically in the order of 10 dB) and very high interferometric coherence on water.

119 citations

Journal ArticleDOI
TL;DR: This work presents models and methods for classification of multiresolution images based on the concept of a reference resolution, corresponding to the highest resolution in the dataset, and proposes a Bayesian framework for classification based on this multiscale model.
Abstract: Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.

64 citations

Journal ArticleDOI
TL;DR: A method that enables incorporation of correlations between images while keeping a good fit to the marginal distributions is proposed, and the joint distributions produced by the transformation method can be used in supervised classification of radar and optical images.
Abstract: With the ever-increasing number and diversity of Earth observation satellites, it steadily becomes more important to be able to analyze compound data sets consisting of different types of images acquired by different sensors. In this paper, we examine different ways of obtaining joint distributions of such images, and we propose a method that enables incorporation of correlations between images while keeping a good fit to the marginal distributions. The approach basically consists of two steps. First, the marginal densities are specified. Based on this specification, each marginal variable is transformed to a normal distributed variable. The joint distribution of the transformed variables is assumed to be multivariate normal. Transforming back to the original scale gives a joint distribution with dependence, where the initial marginal distributions are preserved. The parameters of the new joint distribution can be estimated. The focus is on marginal distributions that are Gamma, K, or Gaussian, although any distribution could be considered. The joint distributions produced by the transformation method can be used in supervised classification of radar and optical images. Results obtained for a set of four-look synthetic aperture radar (SAR) images, as well as a combination of SAR and optical images, are presented.

35 citations


Cited by
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Proceedings Article
01 Jan 1998
TL;DR: In this article, the role of polarimetry in synthetic aperture radar (SAR) interferometry is examined and a coherent decomposition for polarimetric SAR inter-ferometry that allows the separation of the effective phase centers of different scattering mechanisms is introduced.
Abstract: In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

794 citations

Journal ArticleDOI
TL;DR: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework and represents an innovative contribution in the literature.
Abstract: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov-Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the min-cut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial-contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.

678 citations

Journal ArticleDOI
01 Jul 2006
TL;DR: Data from a CL-pol SAR yield to decomposition strategies such as the m-delta method introduced in this paper, which is the architecture of choice for two lunar radars scheduled for launch in 2008.
Abstract: A synthetic aperture radar (SAR) often is constrained to transmit only one polarization. Within this constraint, two aggressive measurement objectives are 1) full characterization and exploitation of the backscattered field, and 2) invariance to geometrical orientations of features in the scene. Full characterization implies coherent dual-polarization to support the four Stokes parameters. These are rotationally invariant with respect backscatterer orientation if and only if the transmission is circularly polarized. Given that the data products are the Stokes parameters, the receivers can use any orthogonal polarization basis. A SAR in hybrid-polarity architecture (CL-pol) transmits circular polarization and receives two orthogonal mutually coherent linear polarizations, which is one manifestation of compact polarimetry. The resulting radar is relatively simple to implement, and has unique self-calibration features and low susceptibility to noise and cross-channel errors. It is the architecture of choice for two lunar radars scheduled for launch in 2008. Data from a CL-pol SAR yield to decomposition strategies such as the m-delta method introduced in this paper.

490 citations

Journal ArticleDOI
TL;DR: A mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data is used, and its accuracy for urban areas at high resolution is proved.
Abstract: 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.

399 citations

Journal ArticleDOI
TL;DR: The Surface Water and Ocean Topography (SWOT) satellite mission is a collaboration between the US National Aeronautics and Space Administration, Centre National d'Etudes Spatiales (the French Spatial Agency), the Canadian Space Agency and the United Kingdom Space Agency, with launch planned in late 2020 as mentioned in this paper.
Abstract: Surface water storage and fluxes in rivers, lakes, reservoirs and wetlands are currently poorly observed at the global scale, even though they represent major components of the water cycle and deeply impact human societies. In situ networks are heterogeneously distributed in space, and many river basins and most lakes—especially in the developing world and in sparsely populated regions—remain unmonitored. Satellite remote sensing has provided useful complementary observations, but no past or current satellite mission has yet been specifically designed to observe, at the global scale, surface water storage change and fluxes. This is the purpose of the planned Surface Water and Ocean Topography (SWOT) satellite mission. SWOT is a collaboration between the (US) National Aeronautics and Space Administration, Centre National d’Etudes Spatiales (the French Spatial Agency), the Canadian Space Agency and the United Kingdom Space Agency, with launch planned in late 2020. SWOT is both a continental hydrology and oceanography mission. However, only the hydrology capabilities of SWOT are discussed here. After a description of the SWOT mission requirements and measurement capabilities, we review the SWOT-related studies concerning land hydrology published to date. Beginning in 2007, studies demonstrated the benefits of SWOT data for river hydrology, both through discharge estimation directly from SWOT measurements and through assimilation of SWOT data into hydrodynamic and hydrology models. A smaller number of studies have also addressed methods for computation of lake and reservoir storage change or have quantified improvements expected from SWOT compared with current knowledge of lake water storage variability. We also briefly review other land hydrology capabilities of SWOT, including those related to transboundary river basins, human water withdrawals and wetland environments. Finally, we discuss additional studies needed before and after the launch of the mission, along with perspectives on a potential successor to SWOT.

395 citations