Effects of the Atmospheric Compensation Method on Hyperspectral Rare Target Detection

This study examines how hyperspectral rare target detection performance is affected by the method of atmospheric compensation used to convert the data to reflectance units. Rare and subpixel target detection algorithms employ contrast enhancement methods to suppress signatures from the background materials. Therefore, when evaluating atmospheric compensation methods, it is important to consider their accuracy in contrast-enhanced space. In particular, a key requirement for good detection is the suppression of atmospheric band residuals in the reflectance spectra, making them as smooth as possible. This explains the success of the empirical Quick Atmospheric Correction (QUAC) algorithm and the importance of supplementing first principles methods with spectral polishing. We illustrate these findings using two data sets acquired by the Rochester Institute of Technology (RIT), three different whitening-based detection algorithms, and three different atmospheric compensation algorithms, QUAC, FLAASH and ATCOR.