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.