Statistical Assessment for Real-time Background Class Identification in Hyperspectral Images

A target material may be considered as an anomaly in an image, having different spectral signatures from the spectral signatures of the background objects. In order to detect such anomalies in an image, the classes associated with the background have to be known. A computationally efficient methodology to determine the background pure classes present in a low resolution hyperspectral image has been previously proposed by the authors. The method is based on robust clustering using a small percentage of the image pixels as input. The clusters are obtained using a self organising map (SOM) clustered using the local minima of the U-matrix (distance matrix). In this paper, we provide a statistical study and evaluation of the proposed approach using simulated and real hyperspectral images. In particular, we answer the question:"what sampling rate should I use in order to be x% confident that I picked up y% of the background classes?".