M.S. Thesis Defense Maria Busuioceanu Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications Monday, 15 July 2013, 10:00 amCarlson Bldg. 3215

M.S. Thesis Defense

Maria Busuioceanu

 

Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications

 

Advisor: Dr. David Messinger

 

Monday, 15 July 2013, 10:00 am 

Carlson Bldg. 3215

 

 

Abstract 

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) CS model 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. 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.