Impact of Signal Contamination on the Adaptive Detection Performance of Local Hyperspectral Anomalies
作者： Stefania Matteoli , Marco Diani , Giovanni Corsini
DOI: 10.1109/TGRS.2013.2256915
关键词: Covariance matrix 、 Detection performance 、 Spectral signature 、 Environmental science 、 Focus (optics) 、 Signal 、 Remote sensing 、 Adaptive filter 、 Hyperspectral imaging 、 Contamination
摘要: The effects of signal contamination secondary data are investigated in the framework adaptive target detection remotely sensed hyperspectral images. In contrast to previous studies on contamination, focus this paper is targets with unknown spectral signatures (i.e., anomalies) and methods based a local estimation background covariance matrix. Contamination due expected have more severe impact when number limited. An analytical model for developed that allows variability extent contamination. Several parameters, such as fraction contaminating energy, introduced, signal-to-interference-plus-noise ratio derived an objective measure proposed employed experimentally evaluate its performance anomalies. outcomes experimental study substantiated by validation real data. results obtained highlight relevance assessed respect different system may practical applications. This represents starting point development forecasting models consider
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