Dr. Wesam A. Sakla Profile

Dr. Wesam A. Sakla

at Lawrence Livermore National Lab

SPIE Involvement:

Author

Publications (16)

Proceedings Article | 8 June 2022 Paper

SAR self-enhanced by electro-optical network (SARSEEN)

Kenneth Tran, Wesam Sakla, Hamid Krim

Proceedings Volume 12122, 121220V (2022) https://doi.org/10.1117/12.2618829

KEYWORDS: Synthetic aperture radar, Image fusion, Sensors, Data modeling, Wavelets, Satellites, RGB color model, Image processing, Satellite imaging, Remote sensing

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Proceedings Article | 20 April 2015 Paper

An empirical comparison of K-SVD and GMRA for dictionary learning

Vipin Vijayan, Wesam Sakla

Proceedings Volume 9477, 94770J (2015) https://doi.org/10.1117/12.2180022

KEYWORDS: Associative arrays, Reconstruction algorithms, Light sources and illumination, Image restoration, Chemical species, Principal component analysis, Compressed sensing, Wavelets, Wavelet transforms, Analytical research

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Proceedings Article | 20 April 2015 Paper

Gaussian weighted neighborhood connectivity of nonlinear line attractor for learning complex manifolds

Theus Aspiras, Vijayan Asari, Wesam Sakla

Proceedings Volume 9477, 94770E (2015) https://doi.org/10.1117/12.2179889

KEYWORDS: Neurons, Neural networks, Databases, Brain, Data modeling, Light sources and illumination, Network architectures, Detection and tracking algorithms, Cameras, Principal component analysis

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Proceedings Article | 20 June 2014 Paper

An objective multi-sensor fusion metric for target detection

S. Sweetnich, S. Fernandes, J. Clark, W. Sakla

Proceedings Volume 9091, 90910M (2014) https://doi.org/10.1117/12.2049936

KEYWORDS: Sensors, Probability theory, Target detection, Data fusion, Image fusion, Image registration, Sensor fusion, Short wave infrared radiation, Sensor performance, Environmental sensing

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Proceedings Article | 13 June 2014 Paper

Sparse representation for vehicle recognition

Nathan Monnig, Wesam Sakla

Proceedings Volume 9090, 90900L (2014) https://doi.org/10.1117/12.2053462

KEYWORDS: Image classification, Detection and tracking algorithms, Light sources and illumination, Principal component analysis, Reconstruction algorithms, Associative arrays, Evolutionary algorithms, Databases, Liquid crystals, Chemical elements

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Proceedings Article | 23 May 2013 Paper

Unmixing hyperspectral skin data using non-negative matrix factorization

Asif Mehmood, Jeffrey Clark, Wesam Sakla

Proceedings Volume 8734, 87340O (2013) https://doi.org/10.1117/12.2016053

KEYWORDS: Skin, Target detection, Principal component analysis, Hyperspectral target detection, Hyperspectral imaging, Matrices, Spectral resolution, Spatial resolution, Data modeling, Feature selection

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Proceedings Article | 10 September 2011 Paper

Vehicle tracking through the exploitation of remote sensing and LWIR polarization science

Hamilton Clouse, Hamid Krim, Wesam Sakla, Olga Mendoza-Schrock

Proceedings Volume 8160, 81600L (2011) https://doi.org/10.1117/12.901556

KEYWORDS: Polarization, Long wavelength infrared, Filtering (signal processing), Feature selection, Infrared radiation, Infrared imaging, Remote sensing, Discrete wavelet transforms, Polarimetry, Thermography

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Proceedings Article | 13 May 2010 Paper

Pattern recognition via multispectral, hyperspectral, and polarization-based imaging

Aed El-Saba, Mohammad Alam, Wesam Sakla

Proceedings Volume 7696, 76961M (2010) https://doi.org/10.1117/12.851689

KEYWORDS: Target detection, Sensors, Polarization, Target recognition, Image fusion, Hyperspectral target detection, Detection and tracking algorithms, Reflectivity, Pattern recognition, Data fusion

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Pattern recognition deals with the detection and identification of a specific target in an unknown input scene. Target features such as shape, color, surface dynamics, and material characteristics are common target attributes used for identification and detection purposes. Pattern recognition using multispectral (MS), hyperspectral (HS), and polarization-based spectral (PS) imaging can be effectively exploited to highlight one or more of these attributes for more efficient target identification and detection. In general, pattern recognition involves two steps: gathering target information from sensor data and identifying and detecting the desired target from sensor data in the presence of noise, clutter, and other artifacts. Multispectral and hyperspectral imaging (MSI/HSI) provide both spectral and spatial information about the target. As the reflection or emission spectral signatures depend on the elemental composition of objects residing within the scene, the polarization state of radiation is sensitive to the surface features such as relative smoothness or roughness, surface material, shapes and edges, etc. Therefore, polarization information imparted by surface reflections of the target yields unique and discriminatory signatures which could be used to augment spectral target detection techniques, through the fusion of sensor data. Sensor data fusion is currently being used to effectively recognize and detect one or more of the target attributes. However, variations between sensors and temporal changes within sensors can introduce noise in the measurements, contributing to additional target variability that hinders the detection process. This paper provides a quick overview of target identification and detection using MSI/HSI, highlighting the advantages and disadvantages of each. It then discusses the effectiveness of using polarization-based imaging in highlighting some of the target attributes at single and multiple spectral bands using polarization spectral imaging (PSI), known as spectropolarimetry imaging.

Proceedings Article | 13 May 2010 Paper

A decision-level fusion scheme using the support vector data description for target detection in hyperspectral imagery

Wesam Sakla, Adel Sakla, Andrew Chan, Jim Ji

Proceedings Volume 7696, 76960Y (2010) https://doi.org/10.1117/12.849936

KEYWORDS: Target detection, Sensors, Hyperspectral target detection, Hyperspectral imaging, Detection and tracking algorithms, Signal to noise ratio, Image fusion, Automatic target recognition, Reflectivity, Data fusion

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Proceedings Article | 13 May 2010 Paper

A support vector data description scheme for hyperspectral target detection using first-order Markov modeling

Wesam Sakla, Adel Sakla, Andrew Chan, Jim Ji

Proceedings Volume 7696, 76960X (2010) https://doi.org/10.1117/12.849934

KEYWORDS: Target detection, Signal to noise ratio, Data modeling, Hyperspectral target detection, Sensors, Detection and tracking algorithms, Automatic target recognition, Reflectivity, Hyperspectral imaging, Statistical modeling

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Proceedings Article | 13 May 2010 Paper

Spectrally-invariant synthetic discriminant signature for hyperspectral target detection using spectral fringe-adjusted joint transform correlation

Aed El-Saba, Adel Sakla, Wesam Sakla

Proceedings Volume 7696, 76961T (2010) https://doi.org/10.1117/12.850520

KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Joint transforms, Hyperspectral target detection, Reflectivity, Spectral resolution, Automatic target recognition, Signal to noise ratio, Hyperspectral imaging

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Proceedings Article | 26 April 2010 Paper

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

Aed El-Saba, Wesam Sakla

Proceedings Volume 7672, 767202 (2010) https://doi.org/10.1117/12.850648

KEYWORDS: Image fusion, Target detection, Target recognition, Polarization, Wavelets, Polarimetry, Sensors, Fourier transforms, Automatic target recognition, Remote sensing

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Proceedings Article | 5 May 2009 Paper

A support vector data description approach to target detection in hyperspectral imagery

Wesam Sakla, Adel Sakla, Andrew Chan

Proceedings Volume 7335, 73350C (2009) https://doi.org/10.1117/12.818642

KEYWORDS: Target detection, Signal to noise ratio, Sensors, Hyperspectral target detection, Detection and tracking algorithms, Hyperspectral imaging, Algorithm development, Target recognition, Automatic target recognition, Matrices

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Proceedings Article | 5 May 2009 Paper

Wavelet-based hyperspectral target detection using spectral fringe-adjusted joint transform correlation

Wesam Sakla, Adel Sakla, M. Alam

Proceedings Volume 7335, 73350B (2009) https://doi.org/10.1117/12.815520

KEYWORDS: Discrete wavelet transforms, Hyperspectral target detection, Detection and tracking algorithms, Wavelets, Reflectivity, Joint transforms, Target detection, Sensors, Hyperspectral imaging, Wavelet transforms

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Proceedings Article | 14 April 2008 Paper

Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation

Wesam Sakla, Adel Sakla, M. Alam

Proceedings Volume 6967, 69670B (2008) https://doi.org/10.1117/12.776550

KEYWORDS: Discrete wavelet transforms, Wavelets, Target detection, Hyperspectral target detection, Joint transforms, Hyperspectral imaging, Detection and tracking algorithms, Wavelet transforms, Sensors, Reflectivity

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Proceedings Article | 17 April 2006 Paper

Enhanced rotation and scale invariant target detection using the fringe-adjusted joint transform correlation

A. El-Saba, M. Alam, W. Sakla

Proceedings Volume 6245, 62450G (2006) https://doi.org/10.1117/12.666614

KEYWORDS: Target detection, MATLAB, Joint transforms, Image filtering, Image processing, Fourier transforms, Composites, Target recognition, Detection and tracking algorithms, Yield improvement

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Showing 5 of 16 publications

Conference Committee Involvement (3)

Pattern Recognition and Tracking XXVIII

12 April 2017 | Anaheim, CA, United States

Optical Pattern Recognition XXVII

20 April 2016 | Baltimore, MD, United States

Optical Pattern Recognition XXVI

22 April 2015 | Baltimore, MD, United States

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