1 June 2005 The AsemiP anomaly detector: comparative performance in hyperspectral imagery
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Abstract
Remote collections of hyperspectral sensor imagery (HSI) often produce extremely large data sets that make storage and transmission difficult. Smart reduction of such a large data set has been a challenge. Automatic anomaly detection has been cited as a suitable method for remote processing of HSI, although automatic anomaly detection using 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, we present the performance of an anomaly detection algorithm known as an approximation to semiparametric (AsemiP) anomaly detector. This detector was conceptualized and developed in the Army Research Laboratory (ARL), where it became a favorite technique for the intended purpose using HSI. This detector uses fundamental theorems of large sample theory to implement a notion of indirect comparison, and it supersedes an earlier ARL technique that uses a semiparametric (SemiP) model, as a basis for statistical inference. The strength of both algorithms is that no prior knowledge is assumed about the target and/or the clutter statistics, albeit AsemiP has an advantage over SemiP of not using an iterative algorithm which is sensitive to arbitrary initial conditions. The AsemiP anomaly detector was tested using real hyperspectral data and compared to alternative techniques, including a benchmark approach, yielding some good results.
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Dalton Rosario, Ruben Galbraith, "The AsemiP anomaly detector: comparative performance in hyperspectral imagery", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.603977; https://doi.org/10.1117/12.603977
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