Recursive automatic target generation process for unsupervised hyperspectral target detection

Abstract

Automatic target generation process (ATGP) has been found very useful and effective for unsupervised target detection. It performs a sequence of orthogonal subspace projection to extract potential targets of interest. One major issue arises in ATGP is how to terminate the algorithm in the sense that how many targets are required for ATGP to generate before it is terminated. This paper presents a recursive version of ATGP, referred to as recursive ATGP (RATGP) which has two advantages. One is no need of inverting any matrix as ATGP does for finding each target. Most importantly, a stopping rule can be derived for ATGP via RATGP is also developed using the Neyman-Pearosn detection theory to determine how many targets needed to be generated by RATGP before it is terminated.