We propose a local anomaly detection strategy for multi-hyperspectral images in which the background probability density function is estimated with a kernel density estimator and locally adaptive information extracted from the image is injected into the bandwidth selection process. Results for multispectral images of different scenarios show the benefits of the proposed strategy regarding its effectiveness both at detecting anomalies and at avoiding the crucial issue of properly selecting the kernel-width parameter.

A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors

DIANI, MARCO;CORSINI, GIOVANNI
2014-01-01

Abstract

We propose a local anomaly detection strategy for multi-hyperspectral images in which the background probability density function is estimated with a kernel density estimator and locally adaptive information extracted from the image is injected into the bandwidth selection process. Results for multispectral images of different scenarios show the benefits of the proposed strategy regarding its effectiveness both at detecting anomalies and at avoiding the crucial issue of properly selecting the kernel-width parameter.
2014
Matteoli, Stefania; Veracini, Tiziana; Diani, Marco; Corsini, Giovanni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/399679
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