On the impact of PCA dimension reduction for hyperspectral detection of difficult targets

Due to constraints both at the sensor and on the ground, dimension reduction is a common preprocessing step performed on many hyperspectral imaging datasets. However, this transformation is not necessarily done with the ultimate data exploitation task in mind-for example, target detection or ground cover classification. Indeed, theoretically speaking it is possible that a lossy operation such as dimension reduction might have a negative impact on detection performance. This notion is investigated experimentally using real-world hyperspectral imaging data. The popular principal components transform [aka. principal components analysis (PCA)] is used to explore the impact that dimension reduction has on adaptive detection of difficult targets in both the reflective and emissive regimes. Using seven state-of-the-art algorithms, it is shown that in many cases PCA can have a minimal impact on the detection statistic value for a target that is spectrally similar to the background against which it is sought.