Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70066
Title: A Novel Implementation of a Hyperspectral Anomaly Detection Algorithm for Real Time Applications with Pushbroom Sensors
Authors: Horstrand, Pablo
Lopez, Sebastian 
Lopez, Jose Fco 
Keywords: Anomaly Detection
Push-Broom Sensors
Real-Time
Issue Date: 2018
Journal: Workshop On Hyperspectral Image And Signal Processing, Evolution In Remote Sensing
Abstract: © 2018 IEEE. Anomaly detection is an increasingly important task when dealing with hyperspectral images in order to distinguish rare objects whose spectral characteristics substantially deviates from those of the neighboring materials. In this paper, a novel technique for accurate detection of anomalies in hyperspectral images is introduced. One of the main features of this method is its ability to process pushbroom data on-the-fly (i.e., line-by-line), being clearly suitable for real time applications in which memory resources are restricted as there is no need to store the whole hypercube. Diverse quality metrics have been applied on testing with real and synthetic hyperspectral data sets in order to compare the accuracy of the proposed algorithm over the state-of-the-art, showing the goodness of our proposal.
URI: http://hdl.handle.net/10553/70066
ISBN: 9781728115818
ISSN: 2158-6276
DOI: 10.1109/WHISPERS.2018.8747221
Source: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing[ISSN 2158-6276],v. 2018-September
Appears in Collections:Actas de congresos
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