A semiparametric approach using the discriminant metric SAM (spectral angle mapper)

摘要

Automatic anomaly detection has been cited as a candidate method for remote processing of hyperspectral sensor imagery (HSI) to promote reduction of the extremely large data sets that make storage and transmission difficult. But automatic anomaly detection in HSI is itself a challenging problem owing to the impact of the atmosphere on spectral content and the variability of spectral signatures. In this paper, I propose to use the discriminant metric SAM (spectral angle mapper) and some of the advances made on the theory of semiparametric inference to design an anomaly detector that assumes no prior knowledge about the target and the clutter statistics. The detector will assume that the probability distribution function (pdf) of any object in a scene can be modeled as a distortion of a reference pdf. The maximum-likelihood method for the model is discussed along with its asymptotic behavior. The proposed anomaly detector is tested using real hyperspectral data and compared to a benchmark approach.