The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Alexander F H Goetz - One of the best experts on this subject based on the ideXlab platform.
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of ima...
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, K B Heidebrecht, A T Shapiro, P J Barloon, J W Boardman, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
Remote Sensing of Environment, 1993Co-Authors: A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H Goetz, Fred A. KruseAbstract:Abstract The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
Fred A. Kruse - One of the best experts on this subject based on the ideXlab platform.
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of ima...
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, K B Heidebrecht, A T Shapiro, P J Barloon, J W Boardman, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
Remote Sensing of Environment, 1993Co-Authors: A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H Goetz, Fred A. KruseAbstract:Abstract The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
Brian M Sadler - One of the best experts on this subject based on the ideXlab platform.
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Spectral Image unmixing from optimal coded aperture compressive measurements
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Ana Ramirez, Gonzalo R Arce, Brian M SadlerAbstract:HyperSpectral remote sensing often captures Imagery where the Spectral profiles of the spatial pixels are the result of the reflectance contribution of numerous materials. Spectral unmixing is then used to extract the collection of materials, or endmembers, contained in the measured spectra and a set of corresponding fractions that indicate the abundance of each material present at each pixel. This paper aims at developing a Spectral unmixing algorithm directly from compressive measurements acquired using the coded-aperture snapshot Spectral imaging (CASSI) system. The proposed method first uses the compressive measurements to find a sparse vector representation of each pixel in a 3-D dictionary formed by a 2-D wavelet basis and a known Spectral library of endmembers. The sparse vector representation is estimated by solving a sparsity-constrained optimization problem using an algorithm based on the variable splitting augmented Lagrangian multipliers method. The performance of the proposed Spectral unmixing method is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The optimal coded apertures are designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Simulations with synthetic and real hyperSpectral cubes illustrate the accuracy of the proposed unmixing method.
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Spectral Image classification from optimal coded aperture compressive measurements
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Ana Ramirez, Henry Arguello, Gonzalo R Arce, Brian M SadlerAbstract:Traditional hyperSpectral imaging sensors acquire high-dimensional data that are used for the discrimination of objects and features in a scene. Recently, a novel architecture known as the coded-aperture snapshot Spectral imaging (CASSI) system has been developed for the acquisition of compressive Spectral Image data with just a few coded focal plane array measurements. This paper focuses on developing a classification approach with hyperSpectral Images directly from CASSI compressive measurements, without first reconstructing the full data cube. The proposed classification method uses the compressive measurements to find the sparse vector representation of the test pixel in a given training dictionary. The estimated sparse vector is obtained by solving a sparsity-constrained optimization problem and is then used to directly determine the class of the unknown pixel. The performance of the proposed classifier is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The set of optimal coded apertures is designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Several simulations illustrate the performance of the proposed classifier using optimal coded apertures and the gain in the classification accuracy obtained over using traditional aperture codes in CASSI.
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Spectral Image unmixing from optimal coded aperture compressive measurements
International Symposium on Communications Control and Signal Processing, 2014Co-Authors: Ana Ramirez, Gonzalo R Arce, Brian M SadlerAbstract:HyperSpectral remote sensing often captures Imagery where the Spectral profiles of the spatial pixels are the result of the reflectance contribution of numerous materials. Spectral unmixing is then used to extract the collection of materials, or endmembers, contained in the measured spectra, and a set of corresponding fractions that indicate the abundance of each material present at each pixel. This work aims at developing a Spectral unmixing algorithm directly from compressive measurements acquired using the coded-aperture snapshot Spectral imaging (CASSI) system. The proposed method first uses the compressive measurements to find a sparse vector representation of each pixel in a 3-D dictionary formed by a 2-D wavelet basis and a known Spectral library of endmembers. The sparse vector representation is estimated by solving a sparsity-constrained optimization problem using an algorithm based on the variable splitting augmented Lagrangian multipliers method. The performance of the proposed Spectral unmixing method is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The optimal coded apertures are designed such that the CASSI sensing matrix satisfies a Restricted Isometry Property (RIP) with high probability. Simulations with synthetic hyperSpectral cubes illustrate the accuracy of the proposed unmixing method.
Rémi Flamary - One of the best experts on this subject based on the ideXlab platform.
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Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Devis Tuia, Alain Rakotomamonjy, Michele Volpi, Mauro Dalla Mura, Rémi FlamaryAbstract:Including spatial information is a key step for successful remote sensing Image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results are boosted. In this paper, we consider the triple objective of designing a spatial/Spectral classifier, which is compact (uses as few features as possible), discriminative (enhances class separation), and robust (works well in small sample situations). We achieve this triple objective by discovering the relevant features in the (possibly infinite) space of spatial filters by optimizing a margin-maximization criterion. Instead of imposing a filter bank with predefined filter types and parameters, we let the model figure out which set of filters is optimal for class separation. To do so, we randomly generate spatial filter banks and use an active-set criterion to rank the candidate features according to their benefits to margin maximization (and, thus, to generalization) if added to the model. Experiments on multiSpectral very high spatial resolution (VHR) and hyperSpectral VHR data show that the proposed algorithm, which is sparse and linear, finds discriminative features and achieves at least the same performances as models using a large filter bank defined in advance by prior knowledge.
A T Shapiro - One of the best experts on this subject based on the ideXlab platform.
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of ima...
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
The earth and space science information system, 1993Co-Authors: Fred A. Kruse, A B Lefkoff, K B Heidebrecht, A T Shapiro, P J Barloon, J W Boardman, Alexander F H GoetzAbstract:The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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the Spectral Image processing system sips interactive visualization and analysis of imaging spectrometer data
Remote Sensing of Environment, 1993Co-Authors: A B Lefkoff, Joseph Boardman, K B Heidebrecht, A T Shapiro, P J Barloon, Alexander F H Goetz, Fred A. KruseAbstract:Abstract The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high Spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
