Adaptive target detection in FLIR imagery using the eigenspace separation transform and principal component analysis

In this paper, an adaptive target detection algorithm for FLIR imagery is proposed that is based on measuring differences between structural information within a target and its surrounding background. At each pixel in the image a dual window is opened where the inner window (inner image vector) represents a possible target signature and the outer window (consisting of a number of outer image vectors) represents the surrounding scene. These image vectors are preprocessed by two directional highpass filters to obtain the corresponding image edge vectors. The target detection problem is formulated as a statistical hypotheses testing problem by mapping these image edge vectors into two transformations, P-1 and P-2, via Eigenspace Separation Transform (EST) and Principal Component Analysis (PCA). The first transformation P-1 is a function of the inner image edge vector. The second transformation P-2 is a function of both the inner and outer image edge vectors. For the hypothesis H-1 (target): the difference of the two functions is small. For the hypothesis H-0 (clutter): the difference of the two functions is large. Results of testing the proposed target detection algorithm on two large FLIR image databases are presented.