Abstract
Target detection in hyperspectral images is important in many applications including search and rescue operations, defense systems, mineral exploration, mine detection and border security. In this study, the goal is to detect the nine sub-pixel targets, from seven different materials, that are placed around the town. For this purpose, eight hyperspectral target detection algorithms are compared and the three most successful algorithms are fused together. The results are compared with ROC curves, and it is found that the fusion of signed ACE, CEM and AMSD algorithms can achieve very successfull results in comparison to the other algorithms.
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