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2007 IEEE International Geoscience and remote sensing Symposium, IGARSS 2007

作者： Hong, Liang Jones, W. Linwood Wilheit, Thomas T. Central Florida Remote Sensing Laboratory University of Central Florida Electrical and Computer Engineering Department Orlando FL 32816-2450 United States 12827 Tall Timber Drive College Station TX 77845 United States

ISBN: (纸本)1424412129

NASA's Global Precipitation Mission will rely on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements. Thus, it is crucial to achieve brightness temperature (Tb) measurement consistency among the constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution;however, since radiometers operate at different frequencies and viewing angles, Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this paper, we investigate two approaches to perform these normalizations from one satellite's measurements to another. One approach is uses Taylor's series expansion around source frequency to predict Tb's of destination frequency. The relationship between Tb's and frequencies are derived from simulations using an oceanic radiative transfer model (RTM) over a wide variety of environmental conditions. The other approach uses a multi-variable linear regression built from RTM Tb simulations among all frequency and V- and H-polarizations. These two approaches are applied in comparing external measurements between WindSat (on Coriolis), TMI (on TRMM) and AMSR (on ADEOS-II). Accuracy prediction and error source analysis are discussed along with calibration results. © 2007 IEEE.

关键词： Satellites

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Return from insects in the clear-air convective boundary layer

Return from insects in the clear-air convective boundary lay...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Robert F. Contreras Stephen J. Frasier Microwave Remote Sensing Laboratory Department of Electrical and Computer Engineering University of Massachusetts Amherst MA USA

The Microwave remote sensing laboratory (MIRSL) at the University of Massachusetts operated its S- band Frequency Modulated Continuous Wave (FMCW) radar during the International H20 Project (IHOP_2002) over the months of May and June 2002. The radar operates at very high spatial and temporal resolutions, 2.4 m and 5 s, respectively, which allows the segregation of scatter from different scattering mechanisms. Rayleigh scattering from particulate scatterers (i.e. dust and insects) dominated the return, however Bragg scattering from turbulence was also significant, especially at the top of the afternoon convective bounday layer (CBL). Scattering from insects was isolated using a 5x5 median high-pass filter. The majority of reflectivity measurements from particulate scatterers ranged from -30 dBZ to -10 dBZ, however intense point-scatterers (> 0 dBZ) skewed the distribution which resulted in mean values much greater than the median values. There is a strong diurnal signal in the backscatter: minima in the morning and at dusk and a maximum mid-afternoon and in the nocturnal boundary layer. The strong diurnal signal suggests that with targeted allocation of radar resources return from insects can be used to monitor the clear-air CBL of the central Great Plains of North America.

关键词： Insects Radar scattering Rayleigh scattering Radar remote sensing remote sensing Laboratories Frequency modulation Spatial resolution Filters Reflectivity

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A NEW GLOBAL REGISTRATION APPROACH OF MEDICAL IMAGING USING VECTOR MAPS

A NEW GLOBAL REGISTRATION APPROACH OF MEDICAL IMAGING USING ...

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IEEE International Symposium on Biomedical Imaging

作者： Hossam E. Abd El Munim Aly A. Farag Computer Vision and Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA

ISBN: (纸本)1424406714

In this paper, we introduce a novel global image registration approach using vector distance functions (VDF's). Edges of the source and target objects are used to represent their images. The VDF's of these edges are calculated as an implicit representation of these boundaries. An energy is formulated to measure the differences of these vectors. A variational formulation is considered to estimate the transformation parameters. Promising results in 2D and 3D are demonstrated to show the efficiency of the proposed algorithm.

关键词： Biomedical imaging image edge detection image registration Parameter estimation computer vision Mutual information Data mining Shape image processing Energy measurement

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Multiresolution manifold learning for classification of hyperspectral data

Multiresolution manifold learning for classification of hype...

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2007 IEEE International Geoscience and remote sensing Symposium, IGARSS 2007

作者： Kim, Wonkook Chen, Yangchi Crawford, Melba M. Tilton, James C. Ghosh, Joydeep Laboratory for Applications of Remote Sensing Purdue University Center for Space Research University of Texas Austin United States Information Science and Technology Research National Aeronautics and Space Administration Department of Electrical and Computer Engineering University of Texas Austin United States

ISBN: (纸本)1424412129

Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dimensional manifold defined by local geometric distances between samples. Most of the traditional methods have focused only on the spectral distances in calculating the local dissimilarity of samples, whereas in the case of image data, the spatial distribution and localized contextual information of image samples could provide useful information. As a framework for integrating spatial and spectral information associated with image samples, a hierarchical spatial-spectral segmentation method is investigated for constructing the manifold structure. The new approach, which develops the manifold for the purpose of classification, incorporates an updating scheme whereby the spatial information and class labels are transferred through the segmentation hierarchy. It is applied to hyperspectral data collected by the Hyperion sensor on the EO-1 satellite over the Okavango Delta of Botswana. Classification accuracies and generalization capability are compared to those achieved by the best basis binary hierarchical classifier, the hierarchical support vector machine classifier, and the shortest path k-nearest neighbor classifier. © 2007 IEEE.

关键词： image classification

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

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关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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Review of existing monographs and books on radar polarimetry and polariemtric SAR with the aim of justifying the need of updates

Review of existing monographs and books on radar polarimetry...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： W.-M. Boerner Jong-Sen Lee Department of Electrical and Computer Engineering University of Illinois Chicago Chicago USA Remote Sensing Division Naval Research Laboratory Inc. WA USA

Radar Polarimetry, Radar Interferometry and Polarimetric SAR Interferometry represent the current culmination in active 'Microwave remote sensing' technology, but we still need to progress considerably more in order to reach the limits of physical realizability. Whereas with radar polarimetry the textural fine-structure, target orientation, symmetries and material constituents can be recovered with considerable improvement above that of standard 'amplitude-only radar', by implementing 'radar interferometry' the spatial (in depth) structure can be explored. With Polarimetric Interferometric Synthetic Aperture Radar (POL-IN-SAR) imaging, it is possible to recover such co-registered textural and spatial information from POL-IN-SAR digital image data sets simultaneously, including the extraction of Digital Elevation Maps (DEM) from either Polarimetric (scattering matrix) or Interferometric (dual antenna) SAR systems. However, in order to further advance this promising technology, we require pertinent basic educational and advanced research texts plus application oriented books for the practicing engineering scientists and users. Hitherto there are not available satisfactory updated sets of basic books or application oriented handbooks. Therefore, in this paper a succinct review of the existing pertinent monographs, books and guides will be presented with the aim of identifying particular topics that still need to be covered. We do now require these urgently associated sets of revised and updated multi-lingual (English, Japanese, Korean, Chinese) books.

关键词： Book reviews Radar polarimetry Synthetic aperture radar Radar interferometry remote sensing Microwave technology Radar imaging Radar remote sensing Synthetic aperture radar interferometry Digital images

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Face Recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

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IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms Pattern recognition Artificial intelligence Particle swarm optimization Computational intelligence computer security Application software image processing

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on image processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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Inter-satellite radiometer calibrations between WindSat, TMI and AMSR

Inter-satellite radiometer calibrations between WindSat, TMI...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Liang Hong W. Linwood Jones Thomas T. Wilheit Central Florida Remote Sensing Laboratory Electrical and Computer Engineering Department University of Central Florida Orlando FL USA Brazos Earth System Science Inc. College Station TX USA

NASA's Global Precipitation Mission will rely on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements. Thus, it is crucial to achieve brightness temperature (T b ) measurement consistency among the constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles,T b normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this paper, we investigate two approaches to perform these normalizations from one satellite's measurements to another. One approach is uses Taylor's series expansion around source frequency to predict T b 's of destination frequency. The relationship between T b 's and frequencies are derived from simulations using an oceanic radiative transfer model (RTM) over a wide variety of environmental conditions. The other approach uses a multi-variable linear regression built from RTMT b simulations among all frequency and V- and H-polarizations. These two approaches are applied in comparing external measurements between WindSat (on Coriolis),TMI (on TRMM) and AMSR (on ADEOS-II). Accuracy prediction and error source analysis are discussed along with calibration results.

关键词： Calibration Sea measurements Microwave radiometry Satellite broadcasting Microwave measurements Brightness temperature Temperature sensors Frequency measurement Performance evaluation Taylor series

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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

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IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424406715;1424406714

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.

关键词： Neoplasms image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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