An assessment of effects of various parameters on target detection using hyperspectral data

Hyperspectral imaging has become a standard for most applications that require precision based analysis. This is due to the fine spectral resolution that hyperspectral data offers. Target detection based on hyperspectral imaging is one of the significant applications required for numerous defence, surveillance as well as many civilian studies. This involves detailed analysis of all bands of hyperspectral data for presence of the desired target. However, there exist many parameters which may have bearing on the performance of detection algorithm. These include sensor related parameters like noise, calibration etc., spatial parameters like size, shape and location etc. and scene parameters like illumination variation, target composition, colour, background etc. This paper demonstrates the implications of three scene parameters namely illumination, background and colour on detection of many known targets using the hyperspectral data. The hyperspectral data acquired over Rochester Institute of Technology (RIT) for experimental purposes has been used. Three popular detection algorithms namely, ACE, MF, SAM have been implemented for target detection and the impact of selected parameters is assessed.