The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Antonio Plaza - One of the best experts on this subject based on the ideXlab platform.
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parallel implementation of a full hyperspectral unmixing chain using opencl
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:Spectral unmixing is an important task for remotely sensed hyperspectral Data Exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spectral signatures for each endmember and their associated abundance fractions. However, unmixing algorithms can be computationally expensive, which compromises their use in applications under real-time constraints. In this paper, we describe a new parallel hyperspectral unmixing chain based on three stages: 1) estimation of the number of endmembers using the geometry-based estimation of number of endmembers algorithm; 2) automatic identification of the spectral signatures of the endmembers using the simplex growing algorithm; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene using the sum-to-one constrained least-squares unmixing algorithm. These algorithms have been specifically selected due to their successful performance in different applications. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative unmixing chain using an hybrid implementation with the OpenCL framework and clMAGMA library. As a result, this is one of the first real-time implementations of a full unmixing chain in an open computing language. This methodology can be executed on different heterogeneous platforms such as CPU (multicore) and GPU platforms, in which accuracy, performance, and power consumption terms have been considered.
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opencl library based implementation of sclsu algorithm for remotely sensed hyperspectral Data Exploitation clmagma versus viennacl
IEEE International Conference on High Performance Computing Data and Analytics, 2016Co-Authors: Sergio Bernabe, Guillermo Botella, Carlos Orueta, Jose M R Navarro, Manuel Prietomatias, Antonio PlazaAbstract:In the last decade, hyperspectral spectral unmixing (HSU) analysis have been applied in many remote sensing applications. For this process, the linear mixture model (LMM) has been the most popular tool used to find pure spectral constituents or endmembers and their fractional abundance in each pixel of the Data set. The unmixing process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be very expensive computationally, a fact that compromises their use in applications under real-time constraints. This is, mainly, due to the last two stages in the unmixing process, which are the most consuming ones. In this work, we propose parallel opencl-library- based implementations of the sum-to-one constrained least squares unmixing (P-SCLSU) algorithm to estimate the per-pixel fractional abundances by using mathematical libraries such as clMAGMA or ViennaCL. To the best of our knowledge, this kind of analysis using OpenCL libraries have not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to achieve efficient implementations using parallel routines. The efficacy of our proposed implementations is demonstrated through Monte Carlo simulations for real Data experiments and using high performance computing (HPC) platforms such as commodity graphics processing units (GPUs).
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parallel implementation of a hyperspectral Data geometry based estimation of number of endmembers algorithm
Proceedings of SPIE, 2016Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyperspectral unmixing has been a challenging task in hyperspectral Data Exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.1 We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic Data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.
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Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs
Journal of Real-Time Image Processing, 2014Co-Authors: Sergio Sánchez, Antonio PlazaAbstract:Spectral unmixing is a very important task for remotely sensed hyperspectral Data Exploitation. It amounts at identifying a set of spectrally pure components (called endmembers ) and their associated per-pixel coverage fractions (called abundances ). A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. Several automatic techniques exist for this purpose, including the virtual dimensionality (VD) concept or the hyperspectral signal identification by minimum error (HySime). Due to the complexity and high dimensionality of hyperspectral scenes, these techniques are computationally expensive. In this paper, we develop new fast implementations of VD and HySime using commodity graphics processing units. The proposed parallel implementations are validated in terms of accuracy and computational performance, showing significant speedups with regards to optimized serial implementations. The newly developed implementations are integrated in a fully operational unmixing chain which exhibits real-time performance with regards to the time that the hyperspectral instrument takes to collect the image Data.
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multi gpu implementation of the minimum volume simplex analysis algorithm for hyperspectral unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Alexander Agathos, Dana Petcu, Antonio PlazaAbstract:Spectral unmixing is an important task in remotely sensed hyperspectral Data Exploitation. The linear mixture model has been widely used to unmix hyperspectral images by identifying a set of pure spectral signatures, called endmembers, and estimating their respective abundances in each pixel of the scene. Several algorithms have been proposed in the recent literature to automatically identify endmembers, even if the original hyperspectral scene does not contain any pure signatures. A popular strategy for endmember identification in highly mixed hyperspectral scenes has been the minimum volume simplex analysis (MVSA), known to be a computationally very expensive algorithm. This algorithm calculates the minimum volume enclosing simplex, as opposed to other algorithms that perform maximum simplex volume analysis (MSVA). The high computational complexity of MVSA, together with its very high memory requirements, has limited its adoption in the hyperspectral imaging community. In this paper, we develop several optimizations to the MVSA algorithm. The main computational task of MVSA is the solution of a quadratic optimization problem with equality and inequality constraints, with the inequality constraints being in the order of the number of pixels multiplied by the number of endmembers. As a result, storing and computing the inequality constraint matrix is highly inefficient. The first optimization presented in this paper uses algebra operations in order to reduce the memory requirements of the algorithm. In the second optimization, we use graphics processing units (GPUs) to effectively solve (in parallel) the quadratic optimization problem involved in the computation of MVSA. In the third optimization, we extend the single GPU implementation to a multi-GPU one, developing a hybrid strategy that distributes the computation while taking advantage of GPU accelerators at each node. The presented optimizations are tested in different analysis scenarios (using both synthetic and real hyperspectral Data) and shown to provide state-of-the-art results from the viewpoint of unmixing accuracy and computational performance. The speedup achieved using the full GPU cluster compared to the CPU implementation in tenfold in a real hyperspectral image.
Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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comparative study and analysis among atgp vca and sga for finding endmembers in hyperspectral imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Chein-i Chang, Shihyu Chen, Hsianmin Chen, Chiahsien WenAbstract:Endmember finding has become increasingly important in hyperspectral Data Exploitation because endmembers can be used to specify unknown particular spectral classes. Pixel purity index (PPI) and N-finder algorithm (N-FINDR) are probably the two most widely used techniques for this purpose where many currently available endmember finding algorithms are indeed derived from these two algorithms and can be considered as their variants. Among them are three well-known algorithms derived from imposing different abundance constraints, that is, abundance-unconstrained automatic target generation process (ATGP), abundance nonnegativity constrained vertex component analysis (VCA), and fully abundance constrained simplex growing algorithm (SGA). This paper explores relationships among these three algorithms and further shows that theoretically they are essentially the same algorithms in the sense of design rationale. The reason that these three algorithms perform differently is not because they are different algorithms, but rather because they use different preprocessing steps, such as initial conditions and dimensionality reduction transforms.
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a theory of recursive orthogonal subspace projection for hyperspectral imaging
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Meiping Song, Chein-i ChangAbstract:Orthogonal subspace projection (OSP) has found many applications in hyperspectral Data Exploitation. Its effectiveness and usefulness result from implementation of two stage processes, i.e., annihilation of undesired signal sources by an OSP via inverting a matrix in the first stage followed by a matched filter to extract the desired signal source in the second stage. This paper presents a theory of recursive OSP (ROSP) for hyperspectral imaging, which performs OSP recursively without inverting undesired signature matrices. This ROSP opens up many new dimensions in extending OSP. First of all, ROSP allows OSP to implement varying signatures via a recursive equation without reinverting undesired signature matrices. Second, ROSP can be further used to derive an unsupervised ROSP (UROSP) OSP, which allows OSP to find a growing number of unknown signal sources recursively while simultaneously determining a desired number of signal sources. As a result, the commonly used automatic target generation process (ATGP) can be extended to a recursive ATGP, which can be considered as a special case of UROSP. Third, for practical applications, UROSP can be also extended in two differ ent fashions to causal process and progressive process, which give rise to causal UROSP and progressive UROSP, respectively, both of which can be easily realized in hardware implementation. Finally, UROSP provides a feasible stopping rule via a recently developed UROSP-specified virtual dimensionality.
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GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral Data Exploitation
Satellite Data Compression Communications and Processing VI, 2010Co-Authors: Sergio Sánchez, Gabriel Martin, Antonio Plaza, Chein-i ChangAbstract:Spectral unmixing is an important task for remotely sensed hyperspectral Data Exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called endmembers, and the material fractions, called fractional abundances, at each pixel of the scene. A standard technique for spectral mixture analysis is linear spectral unmixing, which assumes that the collected spectra at the spectrometer can be expressed in the form of a linear combination of endmembers weighted by their corresponding abundances, expected to obey two constraints, i.e. all abundances should be non-negative, and the sum of abundances for a given pixel should be unity. Several techniques have been developed in the literature for unconstrained, partially constrained and fully constrained linear spectral unmixing, which can be computationally expensive (in particular, for complex highdimensional scenes with a high number of endmembers). In this paper, we develop new parallel implementations of unconstrained, partially constrained and fully constrained linear spectral unmixing algorithms. The implementations have been developed in programmable graphics processing units (GPUs), an exciting development in the field of commodity computing that fits very well the requirements of on-board Data processing scenarios, in which low-weight and low-power integrated components are mandatory to reduce mission payload. Our experiments, conducted with a hyperspectral scene collected over the World Trade Center area in New York City, indicate that the proposed implementations provide relevant speedups over the corresponding serial versions in latest-generation Tesla C1060 GPU architectures.
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an orthogonal subspace projection based for estimation of virtual dimensionality for hyperspectral Data Exploitation
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XIII, 2007Co-Authors: Weimin Liu, Chein-i ChangAbstract:A recently introduced concept, virtual dimensionality (VD) has been shown promise in many applications of hyperspectral Data Exploitation. It was originally developed for estimating number of spectrally distinct signal sources. This paper explores utility of the VD from various signal processing perspectives and further investigates four techniques, Gershgorin radius (GR), orthogonal projection subspace (OSP), signal subspace estimation (SSE), Neyman-Pearson detection (NPD), to be used to estimate the VD. In particular, the OSP-based VD estimation technique is new and has several advantages over other methods. In order to evaluate their performance, a comparative study and analysis is conducted via synthetic and real image experiments.
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hyperspectral Data Exploitation theory and applications
2007Co-Authors: Chein-i ChangAbstract:Preface. Contributors. 1. Overview (Chein-I Chang). I TUTORALS. 2. Hyperspectral Imaging Systems (John P. Kerekes and John R. Schott). 3. Information-Processed Matched Filters for Hyperspectral Target Detection and Classification (Chein-I Chang). II THEORY. 4. An Optical Real-Time Adaptive Spectral Identification System (ORASIS) (Jeffery H. Bowles and David B. Gillis). 5. Stochastic Mixture Modeling (Michael T. Eismann1 and David W. J. Stein). 6. Unmixing Hyperspectral Data: Independent and Dependent Component Analysis (Jose M.P. Nascimento1 and Jose M.B. Dias). 7. Maximum Volume Transform For Endmember Spectra Determination (Michael E. Winter). 8. Hyperspectral Data Representation (Xiuping Jia and John A. Richards). 9. Optimal Band Selection and Utility Evaluation for Spectral Systems (Sylvia S. Shen). 10. Feature Reduction for Classification Purpose (Sebastiano B. Serpico, Gabriele Moser, and Andrea F. Cattoni). 11. Semi-supervised Support Vector Machines for Classification of Hyperspectral Remote Sensing Images (Lorenzo Bruzzone, Mingmin Chi, and Mattia Marconcini). III APPLICATIONS. 12. Decision Fusion for Hyperspectral Classification (Mathieu Fauvel, Jocelyn Chanussot, and Jon Atli Benediktsson) 13. Morphological Hyperspectral Image Classification: A Parallel Processing Perspective (Antonio J. Plaza). 14. Three-Dimensional Wavelet-Based Compression of Hyperspectral Imagery (James E. Fowler and Justin T. Rucker). Index.
Gabriel Martin - One of the best experts on this subject based on the ideXlab platform.
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parallel implementation of a full hyperspectral unmixing chain using opencl
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:Spectral unmixing is an important task for remotely sensed hyperspectral Data Exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spectral signatures for each endmember and their associated abundance fractions. However, unmixing algorithms can be computationally expensive, which compromises their use in applications under real-time constraints. In this paper, we describe a new parallel hyperspectral unmixing chain based on three stages: 1) estimation of the number of endmembers using the geometry-based estimation of number of endmembers algorithm; 2) automatic identification of the spectral signatures of the endmembers using the simplex growing algorithm; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene using the sum-to-one constrained least-squares unmixing algorithm. These algorithms have been specifically selected due to their successful performance in different applications. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative unmixing chain using an hybrid implementation with the OpenCL framework and clMAGMA library. As a result, this is one of the first real-time implementations of a full unmixing chain in an open computing language. This methodology can be executed on different heterogeneous platforms such as CPU (multicore) and GPU platforms, in which accuracy, performance, and power consumption terms have been considered.
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parallel implementation of a hyperspectral Data geometry based estimation of number of endmembers algorithm
Proceedings of SPIE, 2016Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyperspectral unmixing has been a challenging task in hyperspectral Data Exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.1 We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic Data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.
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Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Gabriel Martin, Antonio PlazaAbstract:Linear spectral unmixing is an important task in remotely sensed hyperspectral Data Exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral endmembers from hyperspectral Data, with many of them relying exclusively on the spectral information. In this letter, we develop a novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information. The proposed approach can be combined with any spectral-based endmember extraction technique. Our method is validated using both synthetic scenes constructed using fractals and a real hyperspectral Data set collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite Mining District in Nevada and further compared with previous efforts in the same direction such as the spatial-spectral endmember extraction, automatic morphological endmember extraction, or SPP methods.
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GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral Data Exploitation
Satellite Data Compression Communications and Processing VI, 2010Co-Authors: Sergio Sánchez, Gabriel Martin, Antonio Plaza, Chein-i ChangAbstract:Spectral unmixing is an important task for remotely sensed hyperspectral Data Exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called endmembers, and the material fractions, called fractional abundances, at each pixel of the scene. A standard technique for spectral mixture analysis is linear spectral unmixing, which assumes that the collected spectra at the spectrometer can be expressed in the form of a linear combination of endmembers weighted by their corresponding abundances, expected to obey two constraints, i.e. all abundances should be non-negative, and the sum of abundances for a given pixel should be unity. Several techniques have been developed in the literature for unconstrained, partially constrained and fully constrained linear spectral unmixing, which can be computationally expensive (in particular, for complex highdimensional scenes with a high number of endmembers). In this paper, we develop new parallel implementations of unconstrained, partially constrained and fully constrained linear spectral unmixing algorithms. The implementations have been developed in programmable graphics processing units (GPUs), an exciting development in the field of commodity computing that fits very well the requirements of on-board Data processing scenarios, in which low-weight and low-power integrated components are mandatory to reduce mission payload. Our experiments, conducted with a hyperspectral scene collected over the World Trade Center area in New York City, indicate that the proposed implementations provide relevant speedups over the corresponding serial versions in latest-generation Tesla C1060 GPU architectures.
Sergio Bernabe - One of the best experts on this subject based on the ideXlab platform.
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parallel implementation of a full hyperspectral unmixing chain using opencl
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:Spectral unmixing is an important task for remotely sensed hyperspectral Data Exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spectral signatures for each endmember and their associated abundance fractions. However, unmixing algorithms can be computationally expensive, which compromises their use in applications under real-time constraints. In this paper, we describe a new parallel hyperspectral unmixing chain based on three stages: 1) estimation of the number of endmembers using the geometry-based estimation of number of endmembers algorithm; 2) automatic identification of the spectral signatures of the endmembers using the simplex growing algorithm; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene using the sum-to-one constrained least-squares unmixing algorithm. These algorithms have been specifically selected due to their successful performance in different applications. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative unmixing chain using an hybrid implementation with the OpenCL framework and clMAGMA library. As a result, this is one of the first real-time implementations of a full unmixing chain in an open computing language. This methodology can be executed on different heterogeneous platforms such as CPU (multicore) and GPU platforms, in which accuracy, performance, and power consumption terms have been considered.
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opencl library based implementation of sclsu algorithm for remotely sensed hyperspectral Data Exploitation clmagma versus viennacl
IEEE International Conference on High Performance Computing Data and Analytics, 2016Co-Authors: Sergio Bernabe, Guillermo Botella, Carlos Orueta, Jose M R Navarro, Manuel Prietomatias, Antonio PlazaAbstract:In the last decade, hyperspectral spectral unmixing (HSU) analysis have been applied in many remote sensing applications. For this process, the linear mixture model (LMM) has been the most popular tool used to find pure spectral constituents or endmembers and their fractional abundance in each pixel of the Data set. The unmixing process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be very expensive computationally, a fact that compromises their use in applications under real-time constraints. This is, mainly, due to the last two stages in the unmixing process, which are the most consuming ones. In this work, we propose parallel opencl-library- based implementations of the sum-to-one constrained least squares unmixing (P-SCLSU) algorithm to estimate the per-pixel fractional abundances by using mathematical libraries such as clMAGMA or ViennaCL. To the best of our knowledge, this kind of analysis using OpenCL libraries have not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to achieve efficient implementations using parallel routines. The efficacy of our proposed implementations is demonstrated through Monte Carlo simulations for real Data experiments and using high performance computing (HPC) platforms such as commodity graphics processing units (GPUs).
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parallel implementation of a hyperspectral Data geometry based estimation of number of endmembers algorithm
Proceedings of SPIE, 2016Co-Authors: Sergio Bernabe, Gabriel Martin, Guillermo Botella, Manuel Prietomatias, Antonio PlazaAbstract:In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyperspectral unmixing has been a challenging task in hyperspectral Data Exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.1 We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic Data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.
Padula Gianluca - One of the best experts on this subject based on the ideXlab platform.
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Intuitive Robot Teleoperation through Multi-Sensor Informed Mixed Reality Visual Aids
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Livatino Salvatore, Guastella, Dario C., Muscato Giovanni, Rinaldi Vincenzo, Cantelli Luciano, Melita, Carmelo D., Caniglia Alessandro, Mazza Riccardo, Padula GianlucaAbstract:© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Mobile robotic systems have evolved to include sensors capable of truthfully describing robot status and operating environment as accurately and reliably as never before. This possibility is challenged by effective sensor Data Exploitation, because of the cognitive load an operator is exposed to, due to the large amount of Data and time-dependency constraints. This paper addresses this challenge in remote-vehicle teleoperation by proposing an intuitive way to present sensor Data to users by means of using mixed reality and visual aids within the user interface. We propose a method for organizing information presentation and a set of visual aids to facilitate visual communication of Data in teleoperation control panels. The resulting sensor-information presentation appears coherent and intuitive, making it easier for an operator to catch and comprehend information meaning. This increases situational awareness and speeds up decision-making. Our method is implemented on a real mobile robotic system operating outdoor equipped with on-board internal and external sensors, GPS, and a reconstructed 3D graphical model provided by an assistant drone. Experimentation verified feasibility while intuitive and comprehensive visual communication was confirmed through a qualitative assessment, which encourages further developments.Peer reviewe
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Intuitive Robot Teleoperation through Multi-Sensor Informed Mixed Reality Visual Aids
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Livatino Salvatore, Guastella, Dario C., Muscato Giovanni, Rinaldi Vincenzo, Cantelli Luciano, Melita, Carmelo D., Caniglia Alessandro, Mazza Riccardo, Padula GianlucaAbstract:© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Mobile robotic systems have evolved to include sensors capable of truthfully describing robot status and operating environment as accurately and reliably as never before. This possibility is challenged by effective sensor Data Exploitation, because of the cognitive load an operator is exposed to, due to the large amount of Data and time-dependency constraints. This paper addresses this challenge in remote-vehicle teleoperation by proposing an intuitive way to present sensor Data to users by means of using mixed reality and visual aids within the user interface. We propose a method for organizing information presentation and a set of visual aids to facilitate visual communication of Data in teleoperation control panels. The resulting sensor-information presentation appears coherent and intuitive, making it easier for an operator to catch and comprehend information meaning. This increases situational awareness and speeds up decision-making. Our method is implemented on a real mobile robotic system operating outdoor equipped with on-board internal and external sensors, GPS, and a reconstructed 3D graphical model provided by an assistant drone. Experimentation verified feasibility while intuitive and comprehensive visual communication was confirmed through an assessment, which encourages further developments.Peer reviewe
