A statistical approach to quantifying clutter in hyperspectral infrared images

A method to quantify clutter in hyperspectral infrared (HSI) images in a framework similar to work done on single-band images is presented. Hereby, all objects in a scene that may be mistaken for targets by an automatic target recognition (ATR) algorithm are considered clutter. A hyperspectral image contains a number of contiguous discrete bands within the spectrum. The aim is to obtain a measure of complexity for hyperspectral images, which will indicate the inherent difficulty for an ATR to detect targets. We implemented 129 different image clutter metrics, and computed them for a database of synthesized HSI images. A matched filter ATR was used to determine the amount of clutter in the images as a baseline. We developed a method to select a subset of the metrics that in combination correlated best with the amount of clutter in an image, and defined this as the clutter complexity measure (CCM). Multiple runs of this selection procedure for different training image groups show a dominance of a further subset of metrics that best predict the CCM. Our results also show that the CCM obtained from a varying number of random sample images generalizes well for the entire database.