Comparison of hyperspectral change detection algorithms

There are a multitude of civilian and military applications for the detection of anomalous changes in hyper-spectral images. Anomalous changes occur when the material within a pixel is replaced. Environmental factors that change over time, such as illumination, will affect the radiance of all the pixels in a scene, despite the materials within remaining constant. The goal of an anomalous change detection algorithm is to suppress changes caused by the environment, and detect pixels where the materials within have changed. Anomalous change detection is a two step process. Two co-registered images of a scene are first transformed to maximize the overall correlation between the images, then an anomalous change detector (ACD) is applied to the transformed images. The transforms maximize the correlation between the two images to attenuate the environmental differences that distract from the anomalous changes of importance. Several categories of transforms with different optimization parameters are discussed and compared. One of two types of ACDs are then applied to the transformed images. The first ACD uses the difference of the two transformed images. The second concatenates the spectra of two images and uses an aggregated ACD. A comparison of the two ACD methods and their effectiveness with the different transforms is done for the first time.