Developing an algorithm for local anomaly detection based on spectral space window in hyperspectral image

Zhiyong Li, Jonathan Li, Shilin Zhou, Saied Pirasteh

Abstract: A local anomaly detection algorithm based on sliding windows in spectral space has been proposed in this research. The traditional local anomaly detection algorithms are implemented in spatial windows because local data of an image scene is more suitable for a single statistical model than global data. However, from the aspect of geometric structure of a dataset, this assumption is not entirely proper. As multivariate data, the hyperspectral image dataset can be considered as a low-dimensional manifold, embedded in the high-dimensional spectral space. The nonlinear spectral mixture occurs more frequently, as well as a low dimensional manifold being nonlinear. The traditional spatial local anomaly detection algorithms based on linear projection would not be appropriate to deal with this kind of data. This paper studies the local linear ideas in manifold learning, and an anomaly detection algorithm has been implemented based on the linear projections in a local area of spectral space. The key concept is that a small neighborhood areas of nonlinear manifold can be considered as a local linear structure. The classic spatial local algorithms and proposed algorithm are compared by using real hyperspectral images from vehicle and aviation platforms. The results demonstrated the effectiveness of the proposed algorithm in improving detection of the weak anomalies that decreases the number of false alarms.