Analysis of hyperspectral anomaly change detection algorithms

Abstract

In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT).