Satellite image classification using a modified Metropolis dynamics

Kató, Zoltán; Zerubia, Josiane; Berthod, Marc-Antoine

doi:10.1109/icassp.1992.226148

Cited by 55 publications

(55 citation statements)

References 8 publications

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“…The resolution of the SAR mosaics was 500 m. After this a segmentation step was performed for the daily mosaic. Here we have used an MRF-based segmentation adapted from Kato et al (1992) and Berthod et al (1996), but in practice any feasible segmentation, such as ICM (iterated conditional modes) (Besag, 1986) or even K means (MacQueen, 1967), can be used with rather similar results. The SAR segments smaller than 100 pixels (corresponding to an area of 25 km 2 ) were combined with the adjacent larger segments with the closest HH backscattering value (by an iterative process).…”

Section: Sar Processingmentioning

confidence: 99%

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

Karvonen

2014

The Cryosphere

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Abstract.We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR) segmentation and ice concentration estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based concentration value for each SAR segment. The concentrations are estimated by a multi-layer perceptron (MLP) neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2) polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI) ice chart concentrations and high-resolution AMSR-2 ASI (ARTIST Sea Ice) algorithm concentrations provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm concentrations, which are given in much coarser resolution. The differences when compared to FMI daily ice charts were on average small. When compared to ASI ice concentrations, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the ice edge and sea-land boundary. The main advantage of combining radiometerbased ice concentration estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different ice concentration zones.

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Section: Sar Processingmentioning

confidence: 99%

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

Karvonen

2014

The Cryosphere

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“…(3). For this optimization problem the iterative deterministic modified Metropolis dynamics (MMD) [40] algorithm was adopted. This is a compromise between the deterministic "iterated conditional modes" algorithm (ICM) [36], which is a fast local minimization technique, that is strongly dependent on the initial configuration, and simulated annealing (SA) [37], which is a very computationally intensive global minimization approach.…”

Section: A Dual-pol Casementioning

confidence: 99%

“…This is a compromise between the deterministic "iterated conditional modes" algorithm (ICM) [36], which is a fast local minimization technique, that is strongly dependent on the initial configuration, and simulated annealing (SA) [37], which is a very computationally intensive global minimization approach. MMD is computationally feasible and provides reasonable results in real classification problems [40]. MMD is set with a cooling schedule (like SA) and proceeds as follows:…”

Section: A Dual-pol Casementioning

confidence: 99%

Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas

Krylov

Moser

Zerubia

2011

IEEE J. Sel. Top. Signal Process.

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Abstract-In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene.

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“…This step involves the minimization of the energy (3). For this optimization the deterministic Modified Metropolis Dynamics (MMD) [20] algorithm is suggested. It is a compromise solution between the deterministic Iterated Conditional Modes algorithm [17], which is fast and finds local a minimum, and Simulated Annealing [18], that is slow, but finds the global minimum.…”

Section: Methods Overviewmentioning

confidence: 99%

High resolution SAR-image classification by Markov random fields and finite mixtures

2010

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In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed.

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Satellite image classification using a modified Metropolis dynamics

Cited by 55 publications

References 8 publications

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas

High resolution SAR-image classification by Markov random fields and finite mixtures

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