The Experts below are selected from a list of 38709 Experts worldwide ranked by ideXlab platform
Menguc Oner - One of the best experts on this subject based on the ideXlab platform.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2017Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the Classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two Classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC Classification tasks as a Joint Classification problem.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2016Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the Classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two Classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC Classification tasks as a Joint Classification problem.Publisher's Versio
Ozgur Bayer - One of the best experts on this subject based on the ideXlab platform.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2017Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the Classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two Classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC Classification tasks as a Joint Classification problem.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2016Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the Classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two Classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC Classification tasks as a Joint Classification problem.Publisher's Versio
Sebastiano B. Serpico - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Alessandro Montaldo, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Luca Fronda, Sebastiano B. SerpicoAbstract:In this paper, the problem of the Classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are Jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address co-registered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Index Terms— Satellite image time series, multitemporal Classification, hierarchical multiresolution Markov random fields.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data.
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New cascade model for hierarchical Joint Classification of multisensor and multiresolution remote sensing data
2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:This paper addresses the problem of multisensor fusion of COSMO-SkyMed and RADARSAT-2 data together with optical imagery for Classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered images collected at different spatial resolutions by different sensors. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with a set of images acquired by different SAR sensors, with the aim to characterize the correlations associated with distinct images from different instruments. Experimental results are shown with COSMO-SkyMed, RADARSAT-2, and Pléiades data 1 .
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EUSIPCO - New hierarchical Joint Classification method for SAR-optical multiresolution remote sensing data
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:In this paper, we develop a novel Classification approach for multiresolution, multisensor (optical and synthetic aperture radar), and/or multiband images. Accurate and time-efficient Classification methods are particularly important tools to support rapid and reliable assessment of the ground changes. Given the huge amount and variety of data available currently from last-generation satellite missions, the main difficulty is to develop a classifier that can take benefit of multiband, multiresolution, and multisensor input imagery. The proposed method addresses the problem of multisensor fusion of SAR with optical data for Classification purposes, and allows input data collected at multiple resolutions and additional multiscale features derived through wavelets to be fused.
Ihsen Hedhli - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Alessandro Montaldo, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Luca Fronda, Sebastiano B. SerpicoAbstract:In this paper, the problem of the Classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are Jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address co-registered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Index Terms— Satellite image time series, multitemporal Classification, hierarchical multiresolution Markov random fields.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data.
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New cascade model for hierarchical Joint Classification of multisensor and multiresolution remote sensing data
2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:This paper addresses the problem of multisensor fusion of COSMO-SkyMed and RADARSAT-2 data together with optical imagery for Classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered images collected at different spatial resolutions by different sensors. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with a set of images acquired by different SAR sensors, with the aim to characterize the correlations associated with distinct images from different instruments. Experimental results are shown with COSMO-SkyMed, RADARSAT-2, and Pléiades data 1 .
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EUSIPCO - New hierarchical Joint Classification method for SAR-optical multiresolution remote sensing data
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:In this paper, we develop a novel Classification approach for multiresolution, multisensor (optical and synthetic aperture radar), and/or multiband images. Accurate and time-efficient Classification methods are particularly important tools to support rapid and reliable assessment of the ground changes. Given the huge amount and variety of data available currently from last-generation satellite missions, the main difficulty is to develop a classifier that can take benefit of multiband, multiresolution, and multisensor input imagery. The proposed method addresses the problem of multisensor fusion of SAR with optical data for Classification purposes, and allows input data collected at multiple resolutions and additional multiscale features derived through wavelets to be fused.
Josiane Zerubia - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Alessandro Montaldo, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Luca Fronda, Sebastiano B. SerpicoAbstract:In this paper, the problem of the Classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are Jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address co-registered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Index Terms— Satellite image time series, multitemporal Classification, hierarchical multiresolution Markov random fields.
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. SerpicoAbstract:In this paper, we propose a novel method for the Joint Classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored Classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data.
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New cascade model for hierarchical Joint Classification of multisensor and multiresolution remote sensing data
2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:This paper addresses the problem of multisensor fusion of COSMO-SkyMed and RADARSAT-2 data together with optical imagery for Classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered images collected at different spatial resolutions by different sensors. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with a set of images acquired by different SAR sensors, with the aim to characterize the correlations associated with distinct images from different instruments. Experimental results are shown with COSMO-SkyMed, RADARSAT-2, and Pléiades data 1 .
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EUSIPCO - New hierarchical Joint Classification method for SAR-optical multiresolution remote sensing data
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane ZerubiaAbstract:In this paper, we develop a novel Classification approach for multiresolution, multisensor (optical and synthetic aperture radar), and/or multiband images. Accurate and time-efficient Classification methods are particularly important tools to support rapid and reliable assessment of the ground changes. Given the huge amount and variety of data available currently from last-generation satellite missions, the main difficulty is to develop a classifier that can take benefit of multiband, multiresolution, and multisensor input imagery. The proposed method addresses the problem of multisensor fusion of SAR with optical data for Classification purposes, and allows input data collected at multiple resolutions and additional multiscale features derived through wavelets to be fused.
