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Compressive sensing (CS) takes advantage of the spatial and spectral redundancy in hyperspectral imagery to take fewer measurements than traditional sensors. We simulate compressively sensed hyperspectral airborne images of a HyMap image of Cooke City, Montana using the Coded Aperture Snapshot Spectral Imager Dual Disperser (CASSI-DD) sensor model. Flake et al's novel algorithm (2013), which incorporates...
Accurate detection and clustering are two of the main analysis tasks for remotely sensed spectral imagery. Hyper-spectral image (HSI) analysis often involves mathematically transforming the raw data into a new space using Principal Components Analysis (PCA) or similar techniques where a lower dimensional subspace containing most of the image information may be extracted. The results of standard algorithms...
In recent years, many new methods for analyzing spectral imagery have been introduced. These new methods have been developed to improve the analysis of hyperspectral imagery. Many of these techniques are data driven anomaly/target detection and spectral clustering algorithms which are used to decide whether a particular pixel or area is “interesting.” For this research, a group of these algorithms...
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