Weighted Chebyshev Distance Algorithms for Hyperspectral Target Detection and Classification Applications

Abstract. In this study, an efficient spectral similarity m ethod referred to as Weighted Chebyshev Distance (W CD) method is introduced for supervised classification of hyperspectral imagery (HSI) and target detection applications. The WCD is based on a simple spectral similarity ba sed decision rule using limited amount of reference data. The estimation of upper and lower spectral boundaries o f pectral signatures for all classes across spectr al bands is referred to as a vector tunnel (VT). To obtain the reference information, the training signatures are p ovided randomly from existing data for a known class. Afte r d termination of the parameters of the WCD algori thm with the training set, classification or detection proce dur s are accomplished at each pixel. The comparati ve performances of the algorithms are tested under var ious cases.

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