Güneş, AhmetOzdil, OmerGunes, AhmetEsin, Yunus EmreOzturk, SafakDemirel, BerkanDepartment of Mechatronics Engineering2024-10-062024-10-062018097817281158182158-6268[WOS-DOI-BELIRLENECEK-95]https://hdl.handle.net/20.500.14411/8881Esin, Yunus/0000-0002-3719-9290; Demirel, Berkan/0000-0002-5759-6410Practical target detection systems require an automatic way to detect targets with high accuracy. Detection errors is not tolerable and they should be reduced as much as possible. In classical detection systems, generally single target detection algorithm is performed and the result will be evaluated according to the thresholding techniques. However, in these uncontrolled systems, false alarm rate strongly depends on the thresholding technique success. It is very hard to find a general and constant threshold value for images taken at different conditions and practical detection systems needs reliable threshold value. In this paper, we propose a new multi-stage target detection system which is the combination of different detection algorithms and thresholding technique. This system compose of 4-stages, i.e. namely 1-initial target detection (ACE, GLRT), 2-adaptive Constant False Alarm Rate (CFAR) thresholding, 3-spatially grouping, 4-statistical confidence operation. This system configuration removes the need for interactive user and it automatically implements confirmation and rejection steps. Moreover, this system can be used both for pure pixel and subpixel target detection purposes and it reduces computational processing time considerably with the implementation of consequtive processing stages.eninfo:eu-repo/semantics/closedAccessHyperspectral image processinghyperspectral target detectionCFAR thresholdingfalse alarm mitigationConference ObjectN/AN/AWOS:000482659100079