Fusion of Target Detection Algorithms in Hyperspectral Images

Abstract

Target detection in hyperspectral images is important in many applications including search and rescue operations, defence systems, mineral exploration and border security. For this purpose, several target detection algorithms have been proposed over the years, however, it is not clear which of these algorithms perform best on real data and on sub-pixel targets, and moreover, which of these algorithms have complementary information and should be fused together. The goal of this study is to detect the nine arbitrarily placed sub-pixel targets, from seven different materials from a 1.4km altitude. For this purpose, eight signature-based hyperspectral target detection algorithms, namely the GLRT, ACE, SACE, CEM, MF, AMSD, OSP and HUD, and three anomaly detectors, namely RX, Maxmin and Diffdet, were tested and compared. Among the signature-based target detectors, the three best performing algorithms that have complementary information were identified. Finally these algorithms were fused together using four different fusion algorithms. Our results indicate that with a proper fusion strategy, five of the nine targets could be found with no false alarms.