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Pages
- Title
- Creating a Consistent Oceanic Multi-decadal Intercalibrated TMI-GMI Constellation Data Record.
- Creator
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Chen, Ruiyao, Jones, W Linwood, Mikhael, Wasfy, Wei, Lei, Wilheit, Thomas, McKague, Darren, University of Central Florida
- Abstract / Description
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The Tropical Rainfall Measuring Mission (TRMM), launched in late November 1997 into a low earth orbit, produced the longest microwave radiometric data time series of 17-plus years from the TRMM Microwave Imager (TMI). The Global Precipitation Measuring (GPM) mission is the follow-on to TRMM, designed to provide data continuity and advance precipitation measurement capabilities. The GPM Microwave Imager (GMI) performs as a brightness temperature (Tb) calibration standard for the intersatellite...
Show moreThe Tropical Rainfall Measuring Mission (TRMM), launched in late November 1997 into a low earth orbit, produced the longest microwave radiometric data time series of 17-plus years from the TRMM Microwave Imager (TMI). The Global Precipitation Measuring (GPM) mission is the follow-on to TRMM, designed to provide data continuity and advance precipitation measurement capabilities. The GPM Microwave Imager (GMI) performs as a brightness temperature (Tb) calibration standard for the intersatellite radiometric calibration (XCAL) for the other constellation members; and before GPM was launched, TMI was the XCAL standard. This dissertation aims at creating a consistent oceanic multi-decadal Tb data record that ensures an undeviating long-term precipitation record covering TRMM-GPM eras. As TMI and GMI share only a 13-month common operational period, the U.S. Naval Research Laboratory's WindSat radiometer, launched in 2003 and continuing today provides the calibration bridge between the two. TMI/WindSat XCAL for their (>)9 years' period, and WindSat/GMI XCAL for one year are performed using a robust technique developed by the Central Florida Remote Sensing Lab, named CFRSL XCAL Algorithm, to estimate the Tb bias of one relative to the other. The 3-way XCAL of GMI/TMI/WindSat for their joint overlap period is performed using an extended CFRSL XCAL algorithm. Thus, a multi-decadal oceanic Tb dataset is created. Moreover, an important feature of this dataset is a quantitative estimate of the Tb uncertainty derived from a generic Uncertainty Quantification Model (UQM). In the UQM, various sources contributing to the Tb bias are identified systematically. Next, methods for quantifying uncertainties from these sources are developed and applied individually. Finally, the resulting independent uncertainties are combined into a single overall uncertainty to be associated with the Tb bias on a channel basis. This dissertation work is remarkably important because it provides the science community with a consistent oceanic multi-decadal Tb data record, and also allows the science community to better understand the uncertainty in precipitation products based upon the Tb uncertainties provided.
Show less - Date Issued
- 2018
- Identifier
- CFE0006987, ucf:51650
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006987
- Title
- High Performance Techniques for Face Recognition.
- Creator
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Aldhahab, Ahmed, Mikhael, Wasfy, Atia, George, Jones, W Linwood, Wei, Lei, Elshennawy, Ahmad, University of Central Florida
- Abstract / Description
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The identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, makeup, rotations, illuminations, gestures, etc. Also, facial images contain a great deal of redundant information, which negatively affects the performance of the recognition system. The dimensionality and the redundancy of the facial features have a direct effect on the face recognition accuracy. Not all the features in the feature...
Show moreThe identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, makeup, rotations, illuminations, gestures, etc. Also, facial images contain a great deal of redundant information, which negatively affects the performance of the recognition system. The dimensionality and the redundancy of the facial features have a direct effect on the face recognition accuracy. Not all the features in the feature vector space are useful. For example, non-discriminating features in the feature vector space not only degrade the recognition accuracy but also increase the computational complexity.In the field of computer vision, pattern recognition, and image processing, face recognition has become a popular research topic. This is due to its wide spread applications in security and control, which allow the identified individual to access secure areas, personal information, etc. The performance of any recognition system depends on three factors: 1) the storage requirements, 2) the computational complexity, and 3) the recognition rates.Two different recognition system families are presented and developed in this dissertation. Each family consists of several face recognition systems. Each system contains three main steps, namely, preprocessing, feature extraction, and classification. Several preprocessing steps, such as cropping, facial detection, dividing the facial image into sub-images, etc. are applied to the facial images. This reduces the effect of the irrelevant information (background) and improves the system performance. In this dissertation, either a Neural Network (NN) based classifier or Euclidean distance is used for classification purposes. Five widely used databases, namely, ORL, YALE, FERET, FEI, and LFW, each containing different facial variations, such as light condition, rotations, facial expressions, facial details, etc., are used to evaluate the proposed systems. The experimental results of the proposed systems are analyzed using K-folds Cross Validation (CV).In the family-1, Several systems are proposed for face recognition. Each system employs different integrated tools in the feature extraction step. These tools, Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), 2D or 3D DWT, and Fast Independent Component Analysis (FastICA), are applied to the processed facial images to reduce the dimensionality and to obtain discriminating features. Each proposed system produces a unique representation, and achieves less storage requirements and better performance than the existing methods.For further facial compression, there are three face recognition systems in the second family. Each system uses different integrated tools to obtain better facial representation. The integrated tools, Vector Quantization (VQ), Discrete cosine Transform (DCT), and 2D DWT, are applied to the facial images for further facial compression and better facial representation. In the systems using the tools VQ/2D DCT and VQ/ 2D DWT, each pose in the databases is represented by one centroid with 4*4*16 dimensions. In the third system, VQ/ Facial Part Detection (FPD), each person in the databases is represented by four centroids with 4*Centroids (4*4*16) dimensions. The systems in the family-2 are proposed to further reduce the dimensions of the data compared to the systems in the family-1 while attaining comparable results. For example, in family-1, the integrated tools, FastICA/ 2D DMWT, applied to different combinations of sub-images in the FERET database with K-fold=5 (9 different poses used in the training mode), reduce the dimensions of the database by 97.22% and achieve 99% accuracy. In contrast, the integrated tools, VQ/ FPD, in the family-2 reduce the dimensions of the data by 99.31% and achieve 97.98% accuracy. In this example, the integrated tools, VQ/ FPD, accomplished further data compression and less accuracy compared to those reported by FastICA/ 2D DMWT tools. Various experiments and simulations using MATLAB are applied. The experimental results of both families confirm the improvements in the storage requirements, as well as the recognition rates as compared to some recently reported methods.
Show less - Date Issued
- 2017
- Identifier
- CFE0006709, ucf:51878
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006709
- Title
- An On-orbit Calibration Procedure for Spaceborne Microwave Radiometers Using Special Spacecraft Attitude Maneuvers.
- Creator
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Farrar, Spencer, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Gaiser, Peter, University of Central Florida
- Abstract / Description
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This dissertation revisits, develops, and documents methods that can be used to calibrate spaceborne microwave radiometers once in orbit. The on-orbit calibration methods discussed within this dissertation can provide accurate and early results by utilizing Calibration Attitude Maneuvers (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the Second Stokes (SS) analysis that can provide early and much needed insight on the performance of the instrument. This dissertation...
Show moreThis dissertation revisits, develops, and documents methods that can be used to calibrate spaceborne microwave radiometers once in orbit. The on-orbit calibration methods discussed within this dissertation can provide accurate and early results by utilizing Calibration Attitude Maneuvers (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the Second Stokes (SS) analysis that can provide early and much needed insight on the performance of the instrument. This dissertation describes pre-existing and new methods of using DSC maneuvers as well as a simplified use of the SS procedure. Over TRMM's 17 years of operation it has provided invaluable data and has performed multiple CAMs over its lifetime. These maneuvers are analyzed to implement on-orbit calibration procedures that will be applied for future missions. In addition, this research focuses on the radiometric calibration of TMI that will be incorporated in the final processing (Archive/Legacy of the NASA TMI 1B11 brightness temperature data product). This is of importance since TMI's 17-year sensor data record must be vetted of all known calibration errors so to provide the final stable data for science users, specifically, climatological data records.
Show less - Date Issued
- 2015
- Identifier
- CFE0005611, ucf:50208
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005611
- Title
- A Roughness Correction for Aquarius Ocean Brightness Temperature Using the CONAE MicroWave Radiometer.
- Creator
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Hejazin, Yazan, Jones, W Linwood, Wahid, Parveen, Mikhael, Wasfy, Junek, William, Piepmeier, Jeffrey, University of Central Florida
- Abstract / Description
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Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ)...
Show moreAquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity.In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections.
Show less - Date Issued
- 2015
- Identifier
- CFE0005625, ucf:50218
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005625
- Title
- Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems.
- Creator
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Mcdowell, William, Mikhael, Wasfy, Jones, W Linwood, Haralambous, Michael, Atia, George, Mahalanobis, Abhijit, Muise, Robert, University of Central Florida
- Abstract / Description
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In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D...
Show moreIn this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA (&) LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification.
Show less - Date Issued
- 2015
- Identifier
- CFE0005976, ucf:50790
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005976
- Title
- Understanding images and videos using context.
- Creator
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Vaca Castano, Gonzalo, Da Vitoria Lobo, Niels, Shah, Mubarak, Mikhael, Wasfy, Jones, W Linwood, Wiegand, Rudolf, University of Central Florida
- Abstract / Description
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In computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also...
Show moreIn computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also investigate the influence of the semantic context, determined by objects, relationships, locations, and global composition, to achieve a general understanding of the image content as a whole. In our research, we also attempt to solve the related problem of finding the context associated with visual media. Given a set of visual elements (images), we want to extract the context that can be commonly associated with these images in order to remove ambiguity. The first part of this dissertation concentrates on achieving image understanding using semantic context.(&)nbsp; In spite of the recent success in tasks such as image classi?cation, object detection, image segmentation, and the progress on scene understanding, researchers still lack clarity about computer comprehension of the content of the image as a whole. Hence, we propose a Top-Down Visual Tree (TDVT) image representation that allows the encoding of the content of the image as a hierarchy of objects capturing their importance, co-occurrences, and type of relations. A novel Top-Down Tree LSTM network is presented to learn about the image composition from the training images and their TDVT representations. Given a test image, our algorithm detects objects and determine the hierarchical structure that they form, encoded as a TDVT representation of the image.A single image could have multiple interpretations that may lead to ambiguity about the intentionality of an image.(&)nbsp; What if instead of having only a single image to be interpreted, we have multiple images that represent the same topic. The second part of this dissertation covers how to extract the context information shared by multiple images. We present a method to determine the topic that these images represent. We accomplish this task by transferring tags from an image retrieval database, and by performing operations in the textual space of these tags. As an application, we also present a new image retrieval method that uses multiple images as input. Unlike earlier works that focus either on using just a single query image or using multiple query images with views of the same instance, the new image search paradigm retrieves images based on the underlying concepts that the input images represent.Finally, in the third part of this dissertation, we analyze the influence of context in videos. In this case, the temporal context is utilized to improve scene identification and object detection. We focus on egocentric videos, where agents require some time to change from one location to another. Therefore, we propose a Conditional Random Field (CRF) formulation, which penalizes short-term changes of the scene identity to improve the scene identity accuracy.(&)nbsp; We also show how to improve the object detection outcome by re-scoring the results based on the scene identity of the tested frame. We present a Support Vector Regression (SVR) formulation in the case that explicit knowledge of the scene identity is available during training time. In the case that explicit scene labeling is not available, we propose an LSTM formulation that considers the general appearance of the frame to re-score the object detectors.
Show less - Date Issued
- 2017
- Identifier
- CFE0006922, ucf:51703
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006922
- Title
- Investigation of the effect of rain on sea surface salinity.
- Creator
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Santos Garcia, Andrea, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Junek, William, Asher, William, Wilheit, Thomas, University of Central Florida
- Abstract / Description
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The Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors....
Show moreThe Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors. Thus, it is important to understand these salinity changes due to seawater dilution by rain and the associated near-surface salinity strati?cation. This research addresses the effects of rainfall on the Aquarius (AQ) SSS retrieval using a macro-scale Rain Impact Model (RIM). This model, based on the superposition of a one-dimension eddy diffusion (turbulent diffusion) model, relates SSS to depth, rainfall accumulation and time since rain. To identify instantaneous and prior rainfall accumulations, a Rain Accumulation product was developed. This product, based on the NOAA CMORPH precipitation data set, provides the rainfall history for 24 hours prior to the satellite observation time, which is integrated over each AQ IFOV. In this research results of the RIM validation are presented by comparing AQ and SMOS measured and RIM simulated SSS. The results show the high cross correlation for these comparisons and also with the corresponding SSS anomalies relative to HYCOM.
Show less - Date Issued
- 2016
- Identifier
- CFE0006175, ucf:51133
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006175
- Title
- CONAE MicroWave Radiometer (MWR) Counts to Brightness Temperature Algorithm.
- Creator
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Ghazi, Zoubair, Jones, W Linwood, Wei, Lei, Mikhael, Wasfy, Wu, Thomas, Junek, William, Piepmeier, Jeffrey, University of Central Florida
- Abstract / Description
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This dissertation concerns the development of the MicroWave Radiometer (MWR) brightness temperature (Tb) algorithm and the associated algorithm validation using on-orbit MWR Tb measurements. This research is sponsored by the NASA Earth Sciences Aquarius Mission, a joint international science mission, between NASA and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). The MWR is a CONAE developed passive microwave instrument operating at 23.8 GHz (K-band) H-pol...
Show moreThis dissertation concerns the development of the MicroWave Radiometer (MWR) brightness temperature (Tb) algorithm and the associated algorithm validation using on-orbit MWR Tb measurements. This research is sponsored by the NASA Earth Sciences Aquarius Mission, a joint international science mission, between NASA and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). The MWR is a CONAE developed passive microwave instrument operating at 23.8 GHz (K-band) H-pol and 36.5 GHz (Ka-band) H- (&) V-pol designed to complement the Aquarius L-band radiometer/scatterometer, which is the prime sensor for measuring sea surface salinity (SSS). MWR measures the Earth's brightness temperature and retrieves simultaneous, spatially collocated, environmental measurements (surface wind speed, rain rate, water vapor, and sea ice concentration) to assist in the measurement of SSS.This dissertation research addressed several areas including development of: 1) a signal processing procedure for determining and correcting radiometer system non-linearity; 2) an empirical method to retrieve switch matrix loss coefficients during thermal-vacuum (T/V) radiometric calibration test; and 3) an antenna pattern correction (APC) algorithm using Inter-satellite radiometric cross-calibration of MWR with the WindSat satellite radiometer. The validation of the MWR counts-to-Tb algorithm was performed using two years of on-orbit data, which included special deep space calibration measurements and routine clear sky ocean/land measurements.
Show less - Date Issued
- 2014
- Identifier
- CFE0005496, ucf:50366
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005496
