MARIA GEORGIANA ȘTEFANIA BUSUIOCEANU - Academia.edu (original) (raw)
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Papers by MARIA GEORGIANA ȘTEFANIA BUSUIOCEANU
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors as... more Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions. This thesis utilizes a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) to simulate CS measurements from traditionally sensed HyMap images. A novel reconstruction algorithm that combines spectral smoothing and spatial total variation (TV) is used to create high resolution hyperspectral imagery from the simulated CS measurements. This research examines the effect of the number of measurements, which corresponds to the percentage of physical data sampled, on the quality of simulated CS data as estimated through performance of spectral image processing algorithms. The effect of CS on the data cloud is explored through principal component analysis (PCA) and endmember extraction. The ultimate purpose of this thesis is to investigate the utility of the CS sensor model and reconstruction for various hyperspectral applications in order to identify the strengths and limitations of CS. While CS is shown to create useful imagery for visual analysis, the data cloud is altered and per-pixel spectral fidelity declines for CS reconstructions from only a small number of measurements. In some hyperspectral applications, many measurements are needed in order to obtain comparable results to traditionally sensed HSI, including atmospheric compensation and subpixel target detection. On the other hand, in hyperspectral applications where pixels must be dramatically altered in order to be misclassified, such as land classification or NDVI mapping, CS shows promise. III First and foremost, I would like to express my gratitude to my advisor Dr. David Messinger for all of his guidance and support, without whom my research would not have been possible. I would like to recognize the faculty, staff, and students in the Digital Imaging and Remote Sensing Laboratory for inspiring, motivating, and guiding me throughout my time in the Center for Imaging Science. Additionally, my committee members Dr. John Kerekes and Dr. Nate Cahill deserve a special thanks for contributing their time and expertise to molding my research. Finally, I couldn't have endured these past two years without the support and love of my family and friends.
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors as... more Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions. This thesis utilizes a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) to simulate CS measurements from traditionally sensed HyMap images. A novel reconstruction algorithm that combines spectral smoothing and spatial total variation (TV) is used to create high resolution hyperspectral imagery from the simulated CS measurements. This research examines the effect of the number of measurements, which corresponds to the percentage of physical data sampled, on the quality of simulated CS data as estimated through performance of spectral image processing algorithms. The effect of CS on the data cloud is explored through principal component analysis (PCA) and endmember extraction. The ultimate purpose of this thesis is to investigate the utility of the CS sensor model and reconstruction for various hyperspectral applications in order to identify the strengths and limitations of CS. While CS is shown to create useful imagery for visual analysis, the data cloud is altered and per-pixel spectral fidelity declines for CS reconstructions from only a small number of measurements. In some hyperspectral applications, many measurements are needed in order to obtain comparable results to traditionally sensed HSI, including atmospheric compensation and subpixel target detection. On the other hand, in hyperspectral applications where pixels must be dramatically altered in order to be misclassified, such as land classification or NDVI mapping, CS shows promise. III First and foremost, I would like to express my gratitude to my advisor Dr. David Messinger for all of his guidance and support, without whom my research would not have been possible. I would like to recognize the faculty, staff, and students in the Digital Imaging and Remote Sensing Laboratory for inspiring, motivating, and guiding me throughout my time in the Center for Imaging Science. Additionally, my committee members Dr. John Kerekes and Dr. Nate Cahill deserve a special thanks for contributing their time and expertise to molding my research. Finally, I couldn't have endured these past two years without the support and love of my family and friends.