A Statistical Approach for Multiclass Target Detection

Abstract

Fukunaga-Koontz Transform (FKT) is a statistical technique which has many application areas for two-class classification or detection problems. In this paper, we have proposed improved target detection algorithm for hyperspectral imagery (HSI) based on enhanced FKT which gives better results for multi-class target detection problems.Hyperspectral imagery is popular for target detection applications due to its additional properties compare to multispectral images. It presents one dimensional (spectral) and two dimensional (spatial) features of targets for detection problems. For multi-class target detection in hyperspectral images, we have selected each target's 1D features, called signature of targets, to introduce to enhanced FKT during learning stage. After learning stage of FKT, we have applied our operators, obtained by enhanced FKT, to HSI images to detect more than one target simultaneously.The experimental results show that multi-class FKT has satisfactory performance over 95 percent of true detection rate especially on the pixel sized targets. In addition, multi-class processing ability of the proposed enhanced FKT is very important for many applications such as classification, recognition, clustering, tracking problems in the literature.