The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Maurizio Migliaccio - One of the best experts on this subject based on the ideXlab platform.
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x band two scale sea surface scattering Model to predict the Contrast due to an oil slick
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Antonio Montuori, Maurizio Migliaccio, Ferdinando Nunziata, Piotr SobieskiAbstract:In this study, a sea/oil Contrast Model, based on the two-scale sea surface scattering Boundary Perturbation Model and an improved Marangoni damping Model, is exploited to predict the X-band Contrast due to an oil slick. Theoretical predictions are then compared with actual X-band synthetic aperture radar (SAR) measurements collected by COSMO-SkyMed and TerraSAR-X satellites over the polluted area off the Aberdeen coast (United Kingdom) during the Gannet Alpha oil spillage occurred in 2011. The Contrast Model is here verified at X-band for the first time and exploited in a very challenging scenario, i.e., when an oil slick is in place. In addition, a detailed analysis on the effect of sensor's noise equivalent sigma zero (NESZ) on the predicted and measured Contrast is undertaken. Experimental results confirm Model predictions, witnessing a remarkable agreement between predicted and measured Contrasts. Moreover, they demonstrate that NESZ significantly affects the information content of the signal backscattered off the oil-covered area.
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the two scale bpm scattering Model for sea biogenic slicks Contrast
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: Ferdinando Nunziata, Piotr Sobieski, Maurizio MigliaccioAbstract:Sea oil slick observation by means of synthetic aperture radar is still a scientific and operational challenge. In this paper, the sea surface scattering with and without biogenic slicks is analyzed by using the two-scale Boundary Perturbation Method scattering Model. The surface slick is supposed to modify both the full-range sea surface spectrum and the slope probability density function by means of the Marangoni damping and by a reduced friction velocity. In this paper, the full-range Universite Catholique de Louvain sea surface spectrum is considered. A new Contrast Model, which overcomes the drawbacks of the Contrast Model based on the untilted Small Perturbation Method scattering Model, is presented and illustrated in some L- and C-band biogenic slick cases.
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IGARSS (4) - A BPM Two-Scale Contrast Model
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Ferdinando Nunziata, Maurizio Migliaccio, Piotr SobieskiAbstract:This paper describes a new two-scale polarimetric Contrast Model based on the Boundary Perturbation Model (BPM). The damping of the short gravity-capillary surface waves by small slicks is Modelled by the Marangoni damping coefficient. The surface slick is supposed to modify both the short wave part of the sea surface spectrum intensity and the wind input. The Model has been validated over SIR-C/X-SAR Multi Look Complex (MLC) L- and C-band Synthetic Aperture Radar (SAR) data.
Piotr Sobieski - One of the best experts on this subject based on the ideXlab platform.
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x band two scale sea surface scattering Model to predict the Contrast due to an oil slick
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Antonio Montuori, Maurizio Migliaccio, Ferdinando Nunziata, Piotr SobieskiAbstract:In this study, a sea/oil Contrast Model, based on the two-scale sea surface scattering Boundary Perturbation Model and an improved Marangoni damping Model, is exploited to predict the X-band Contrast due to an oil slick. Theoretical predictions are then compared with actual X-band synthetic aperture radar (SAR) measurements collected by COSMO-SkyMed and TerraSAR-X satellites over the polluted area off the Aberdeen coast (United Kingdom) during the Gannet Alpha oil spillage occurred in 2011. The Contrast Model is here verified at X-band for the first time and exploited in a very challenging scenario, i.e., when an oil slick is in place. In addition, a detailed analysis on the effect of sensor's noise equivalent sigma zero (NESZ) on the predicted and measured Contrast is undertaken. Experimental results confirm Model predictions, witnessing a remarkable agreement between predicted and measured Contrasts. Moreover, they demonstrate that NESZ significantly affects the information content of the signal backscattered off the oil-covered area.
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the two scale bpm scattering Model for sea biogenic slicks Contrast
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: Ferdinando Nunziata, Piotr Sobieski, Maurizio MigliaccioAbstract:Sea oil slick observation by means of synthetic aperture radar is still a scientific and operational challenge. In this paper, the sea surface scattering with and without biogenic slicks is analyzed by using the two-scale Boundary Perturbation Method scattering Model. The surface slick is supposed to modify both the full-range sea surface spectrum and the slope probability density function by means of the Marangoni damping and by a reduced friction velocity. In this paper, the full-range Universite Catholique de Louvain sea surface spectrum is considered. A new Contrast Model, which overcomes the drawbacks of the Contrast Model based on the untilted Small Perturbation Method scattering Model, is presented and illustrated in some L- and C-band biogenic slick cases.
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IGARSS (4) - A BPM Two-Scale Contrast Model
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Ferdinando Nunziata, Maurizio Migliaccio, Piotr SobieskiAbstract:This paper describes a new two-scale polarimetric Contrast Model based on the Boundary Perturbation Model (BPM). The damping of the short gravity-capillary surface waves by small slicks is Modelled by the Marangoni damping coefficient. The surface slick is supposed to modify both the short wave part of the sea surface spectrum intensity and the wind input. The Model has been validated over SIR-C/X-SAR Multi Look Complex (MLC) L- and C-band Synthetic Aperture Radar (SAR) data.
Ferdinando Nunziata - One of the best experts on this subject based on the ideXlab platform.
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x band two scale sea surface scattering Model to predict the Contrast due to an oil slick
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Antonio Montuori, Maurizio Migliaccio, Ferdinando Nunziata, Piotr SobieskiAbstract:In this study, a sea/oil Contrast Model, based on the two-scale sea surface scattering Boundary Perturbation Model and an improved Marangoni damping Model, is exploited to predict the X-band Contrast due to an oil slick. Theoretical predictions are then compared with actual X-band synthetic aperture radar (SAR) measurements collected by COSMO-SkyMed and TerraSAR-X satellites over the polluted area off the Aberdeen coast (United Kingdom) during the Gannet Alpha oil spillage occurred in 2011. The Contrast Model is here verified at X-band for the first time and exploited in a very challenging scenario, i.e., when an oil slick is in place. In addition, a detailed analysis on the effect of sensor's noise equivalent sigma zero (NESZ) on the predicted and measured Contrast is undertaken. Experimental results confirm Model predictions, witnessing a remarkable agreement between predicted and measured Contrasts. Moreover, they demonstrate that NESZ significantly affects the information content of the signal backscattered off the oil-covered area.
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the two scale bpm scattering Model for sea biogenic slicks Contrast
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: Ferdinando Nunziata, Piotr Sobieski, Maurizio MigliaccioAbstract:Sea oil slick observation by means of synthetic aperture radar is still a scientific and operational challenge. In this paper, the sea surface scattering with and without biogenic slicks is analyzed by using the two-scale Boundary Perturbation Method scattering Model. The surface slick is supposed to modify both the full-range sea surface spectrum and the slope probability density function by means of the Marangoni damping and by a reduced friction velocity. In this paper, the full-range Universite Catholique de Louvain sea surface spectrum is considered. A new Contrast Model, which overcomes the drawbacks of the Contrast Model based on the untilted Small Perturbation Method scattering Model, is presented and illustrated in some L- and C-band biogenic slick cases.
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IGARSS (4) - A BPM Two-Scale Contrast Model
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Ferdinando Nunziata, Maurizio Migliaccio, Piotr SobieskiAbstract:This paper describes a new two-scale polarimetric Contrast Model based on the Boundary Perturbation Model (BPM). The damping of the short gravity-capillary surface waves by small slicks is Modelled by the Marangoni damping coefficient. The surface slick is supposed to modify both the short wave part of the sea surface spectrum intensity and the wind input. The Model has been validated over SIR-C/X-SAR Multi Look Complex (MLC) L- and C-band Synthetic Aperture Radar (SAR) data.
Antonio Montuori - One of the best experts on this subject based on the ideXlab platform.
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x band two scale sea surface scattering Model to predict the Contrast due to an oil slick
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Antonio Montuori, Maurizio Migliaccio, Ferdinando Nunziata, Piotr SobieskiAbstract:In this study, a sea/oil Contrast Model, based on the two-scale sea surface scattering Boundary Perturbation Model and an improved Marangoni damping Model, is exploited to predict the X-band Contrast due to an oil slick. Theoretical predictions are then compared with actual X-band synthetic aperture radar (SAR) measurements collected by COSMO-SkyMed and TerraSAR-X satellites over the polluted area off the Aberdeen coast (United Kingdom) during the Gannet Alpha oil spillage occurred in 2011. The Contrast Model is here verified at X-band for the first time and exploited in a very challenging scenario, i.e., when an oil slick is in place. In addition, a detailed analysis on the effect of sensor's noise equivalent sigma zero (NESZ) on the predicted and measured Contrast is undertaken. Experimental results confirm Model predictions, witnessing a remarkable agreement between predicted and measured Contrasts. Moreover, they demonstrate that NESZ significantly affects the information content of the signal backscattered off the oil-covered area.
Daniel J. Navarro - One of the best experts on this subject based on the ideXlab platform.
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Common and distinctive features in stimulus similarity: A modified version of the Contrast Model
Psychonomic Bulletin & Review, 2004Co-Authors: Daniel J. NavarroAbstract:Featural representations of similarity data assume that people represent stimuli in terms of a set of discrete properties. In this article, we consider the differences in featural representations that arise from making four different assumptions about how similarity is measured. Three of these similarity Models— the common features Model, the distinctive features Model, and Tversky’s seminal Contrast Model—have been considered previously. The other Model is new and modifies the Contrast Model by assuming that each individual feature only ever acts as a common or distinctive feature. Each of the four Models is tested on previously examined similarity data, relating to kinship terms, and on a new data set, relating to faces. In fitting the Models, we have used the geometric complexity criterion to balance the competing demands of data-fit and Model complexity. The results show that both common and distinctive features are important for stimulus representation, and we argue that the modified Contrast Model combines these two components in a more effective and interpretable way than Tversky’s original formulation.
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Clustering Using the Contrast Model
2001Co-Authors: Daniel J. Navarro, Michael D. LeeAbstract:An algorithm is developed for generating featural representations from similarity data using Tversky’s (1977) Contrast Model. Unlike previous additive clustering approaches, the algorithm fits a representational Model that allows for stimulus similarity to be measured in terms of both common and distinctive features. The important issue of striking an appropriate balance between data fit and representational complexity is addressed through the use of the Geometric Complexity Criterion to guide Model selection. The ability of the algorithm to recover known featural representations from noisy data is tested, and it is also applied to real data measuring the similarity of
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Clustering Using the Contrast Model - eScholarship
2001Co-Authors: Daniel J. Navarro, Michael D. LeeAbstract:Clustering Using the Contrast Model Daniel J. Navarro and Michael D. Lee daniel.navarro,michael.lee @psychology.adelaide.edu.au Department of Psychology, University of Adelaide South Australia, 5005, AUSTRALIA Abstract An algorithm is developed for generating featural rep- resentations from similarity data using Tversky’s (1977) Contrast Model. Unlike previous additive clustering ap- proaches, the algorithm fits a representational Model that allows for stimulus similarity to be measured in terms of both common and distinctive features. The important is- sue of striking an appropriate balance between data fit and representational complexity is addressed through the use of the Geometric Complexity Criterion to guide Model selection. The ability of the algorithm to recover known featural representations from noisy data is tested, and it is also applied to real data measuring the similarity of kin- ship terms. Introduction Understanding human mental representation is necessary for understanding human perception, cognition, decision making, and action. Mental representations play an im- portant role in mediating adaptive behavior, and form the basis for the cognitive processes of generalization, infer- ence and learning. Different assumptions regarding the nature and form of mental representation lead to different constraints on formal Models of these processes. For this reason, Pinker (1998) argues that “pinning down men- tal representation is the route to rigor in psychology” (p. 85). Certainly, it is important that cognitive Models use principled mental representations, since the ad hoc defi- nition of stimuli on the basis of intuitive reasonableness is a highly questionable practice (Brooks 1991, Komatsu 1992, Lee 1998). One appealing and widely used approach for deriving stimulus representations is to base them on measures of stimulus similarity. Following Shepard (1987), similarity may be understood as a measure of the degree to which the consequences of one stimulus generalize to another, and so it makes adaptive sense to give more similar stim- uli mental representations that are themselves more sim- ilar. For a domain with n stimuli, similarity data take the form of an n × n similarity matrix, S = s i j , where s i j is the similarity of the ith and jth stimuli. The goal of similarity-based representation is then to define stimulus representations that, under a given similarity Model, cap- ture the constraints implicit in the similarity matrix by approximating the data. Goldstone’s (in press) recent review identifies four broad Model classes for stimulus similarity: geomet- ric, featural, alignment-based, and transformational. Of these, the two most widely used approaches are the ge- ometric, where stimuli are represented in terms of their values on different dimensions, and the featural, where stimuli are represented in terms of the presence or ab- sence of weighted features. The geometric approach is most often used in formal Models of cognitive pro- cesses, partly because of the ready availability of tech- niques such as multidimensional scaling (e.g., Kruskal 1964; see Cox & Cox 1994 for an overview), which gen- erate geometric representations from similarity data. The featural approach to stimulus representation, however, is at least as important as the geometric approach, and war- rants the development of techniques analogous to multi- dimensional scaling. Accordingly, this paper describes an algorithm that generates featural representations from similarity data. The optimization processes used in the algorithm are standard ones, and could almost certainly be improved. In this regard, we draw on Shepard and Arabie’s (1979) distinction between the psychological Model that is be- ing fit, and the algorithm that does the fitting. We make no claims regarding the significance of the algorithm it- self (and certainly do not claim it is a Model of the way humans learn mental representations), but believe that the psychological representational Model that it fits has three important properties. First, it allows for the arbi- trary definition of features, avoiding the limitations of partitioning or hierarchical clustering. Second, it uses a more general Model of featural stimulus similarity than has previously been considered. Third, it generates feat- ural representations in a way that balances the competing demands of data-fit and representational complexity. Featural Representation Within a featural representation, stimuli are defined by the presence or absence of a set of saliency weighted fea- tures or properties. Formally, if a stimulus domain con- tains n stimuli and m features, a featural representation is given by the n × m matrix F = f ik , where f ik = if stimulus i has feature k otherwise, together with a vector w = (w 1 , . . . , w m ) giving the (pos- itive) weights of each of the features.