The Experts below are selected from a list of 181818 Experts worldwide ranked by ideXlab platform
Brian L Mark - One of the best experts on this subject based on the ideXlab platform.
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joint spatial temporal spectrum sensing for cognitive radio networks
IEEE Transactions on Vehicular Technology, 2010Co-Authors: Brian L MarkAbstract:In a wireless system with opportunistic spectrum sharing, secondary users equipped with cognitive radios attempt to access a radio spectrum that is not being used by the primary licensed users. On a given Frequency Channel, a secondary user can perform spectrum sensing to determine spatial or temporal opportunities for spectrum reuse. Whereas most prior works address either spatial or temporal sensing in isolation, we propose a joint spatial-temporal spectrum-sensing scheme that exploits information from spatial sensing to improve the performance of temporal sensing. We quantify the performance benefit of the joint spatial-temporal scheme over pure spatial sensing and pure temporal sensing based on counting-rule and linear quadratic (LQ) detectors. Finally, we analyze a multilevel quantization feedback scheme that can improve the performance of temporal sensing based on counting-rule detectors.
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estimation of maximum interference free power level for opportunistic spectrum access
IEEE Transactions on Wireless Communications, 2009Co-Authors: Brian L Mark, Ahmed O. NasifAbstract:We consider a scenario in which Frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme for a group of Frequency agile radios to estimate the maximum power at which they can transmit on a given Frequency Channel, without causing harmful interference to the primary receivers. The proposed scheme relies on signal strength measurements taken by a group of Frequency agile radios, which are then used by a target node to characterize the spatial size of its perceived spectrum hole in terms of the maximum permissible transmit power. We derive an approximation to the maximum interference-free transmit power using the Cramer-Rao bound on localization accuracy. We present numerical results to demonstrate the effectiveness of the proposed scheme under a variety of scenarios.
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joint spatial temporal spectrum sensing for cognitive radio networks
Conference on Information Sciences and Systems, 2009Co-Authors: Brian L MarkAbstract:In a wireless system with opportunistic spectrum sharing, secondary users equipped with cognitive radios attempt to access radio spectrum that is not being used by the primary licensed users. On a given Frequency Channel, a secondary user can perform spectrum sensing to determine spatial or temporal opportunities for spectrum reuse. Whereas most prior works address either spatial or temporal sensing in isolation, we propose a joint spatial-temporal spectrum sensing scheme, which exploits information from spatial sensing to improve the performance of temporal sensing. Our simulation results show that the proposed joint spatial-temporal approach significantly outperforms existing temporal sensing schemes.
Ran Tao - One of the best experts on this subject based on the ideXlab platform.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the Frequency domain and the original spatial Channel features (e.g., color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in Frequency domain and the original spatial Channel features (e.g., color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.
Danfeng Hong - One of the best experts on this subject based on the ideXlab platform.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the Frequency domain and the original spatial Channel features (e.g., color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in Frequency domain and the original spatial Channel features (e.g., color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.
Jiaojiao Tian - One of the best experts on this subject based on the ideXlab platform.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the Frequency domain and the original spatial Channel features (e.g., color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in Frequency domain and the original spatial Channel features (e.g., color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.
Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the Frequency domain and the original spatial Channel features (e.g., color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
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orsim detector a novel object detection framework in optical remote sensing imagery using spatial Frequency Channel features
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran TaoAbstract:With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-Frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in Frequency domain and the original spatial Channel features (e.g., color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.