Detecting targets with unknown spectral signatures in hyperspectral imagery has been proven to be a topic of great interest in several applications. Because no knowledge about the targets of interest is assumed, this task is performed by searching the image for anomalous pixels, i.e. those pixels deviating from a statistical model of the background. In this thesis work, a new scheme is proposed for detecting both global and local anomalies.
After I received my Bachelor's and Master's degrees in Telecommunications Engineering, I obtained my PhD degree in Remote Sensing. At the moment, I am System Analyst in IDS. I have developed and designed systems aimed at automatic information retrieval from hyper- and multi-spectral, Infrared and SAR images acquired by satellite or aerial vehicles.
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LAP LAMBERT Academic Publishing
Remote sensing, adaptive estimation, Hyperspectral imaging, image fusion, image sensors, Probability
SCIENCE / Earth Sciences