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EnglishWe 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.
| Autori interni: | ||
| Autori: | Matteoli, Stefania; Veracini, Tiziana; Diani, Marco; Corsini, Giovanni | |
| Titolo: | A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors | |
| Anno del prodotto: | 2014 | |
| 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. | |
| Digital Object Identifier (DOI): | 10.1109/LGRS.2013.2257670 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| AlocallyAdaptive.pdf | Versione finale editoriale | NON PUBBLICO - Accesso privato/ristretto | Utenti riconosciuti Richiedi una copia |
http://hdl.handle.net/11568/399679
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