The target implant method for predicting target difficulty and detector performance in hyperspectral imagery

摘要

The utility of a hyperspectral image for target detection can be measured by synthetically implanting target spectra in the image and applying detection algorithms.1 In this paper we apply this method, called the target implant method, for the purpose of determining the top performing algorithms for a given image and given target and for determining the relative difficulty for detection of targets in a given image with a given detector. Our tests include variations on the matched filter, adaptive coherence/cosine estimator and constrained energy minimization detection algorithms. This enables one to predict the fill fraction at which a given target can be detected and the best detection algorithm in a given image under ideal circumstances. Comparison of predictions from this method to detection performance on real target pixels shows that the target implant method does provide accurate relative predictions in terms of both target difficulty and detector performance, but reliably predicting the actual number of false alarms for a given target at a given fill fraction is difficult or impossible. In our tests we used images from the Cooke City Collection2,3 and from the Forest Radiance Collection.4 The Cooke City Collection was taken with the HyMap sensor on July 4, 2006. This imagery has 126 bands ranging from 453.8 to 2496.3 nm at a ground sample distance of approximately 3 meters. Seven flightlines were collected, six of which contain 4 fabric target panels and 3 vehicles with known spectra. The Forest Radiance imagery had 210 spectral bands (145 good bands) ranging from 397.4nm to 2496.5 with a ground sample distance of approximately 1.9 meters