Abstract
In this paper we discuss our approach to winning entries to the RIT blind test competition. The image cube was preprocessed using a spatial filter that changed the sharpness and enhanced and isolated small point like features. This spatially sharpened cube was then processed using the ENVI hour glass algorithm and obtained high probability of detection and a small probability of false alarm for the blind test targets. In a simulation we quantified this result using metrics related to the Receiver Operator Characteristics (ROC) curve analysis. A hyper-spectral data cube was created and sub-pixel targets were inserted. We found that sharpening the hyper-spectral cube increases the number of correctly identified sub-pixel targets compared to no pre-processing. In particular the simple un-sharp masking filter generates excellent results. We propose that all sub-pixel target detection algorithms could benefit from sharpening of the spectral cube.
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