A Novel Hyperspectral Anomaly Detection Algorithm for Real-Time Applications With Push-Broom Sensors

Most practical hyperspectral anomaly detection (AD) applications require real-time processing for detecting complex targets from their background. This is especially critical in defense and surveillance domains, but also in many other scenarios, in which a rapid response is mandatory to save human l...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 12; no. 12; pp. 4787 - 4797
Main Authors: Horstrand, Pablo, Diaz, Maria, Guerra, Raul, Lopez, Sebastian, Lopez, Jose Fco
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anomalies
Anomaly detection
Anomaly detection (AD)
Complexity
Detection
Drones
hyperspectral imagery
Hyperspectral imaging
Image acquisition
Imaging techniques
onboard processing
push-broom sensor
Pushbroom sensor modes
Real time
Real-time systems
Sensors
Target detection
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Most practical hyperspectral anomaly detection (AD) applications require real-time processing for detecting complex targets from their background. This is especially critical in defense and surveillance domains, but also in many other scenarios, in which a rapid response is mandatory to save human lives. Dealing with such a high dimensionality of data requires the conception of new algorithms to ease the demanding computing performance. Push-broom scanning represents the mainstream in hyperspectral imaging, introducing added complexity to the equation as there is no information of future pixels. In this paper, a novel technique named line-by-line anomaly detection (LbL-AD) algorithm, is presented as a way of performing real-time processing with a push-broom sensor. The sensor has been mounted on an unmanned aerial vehicle, and the acquired images, together with others from the scientific literature and synthetic ones, have been used to extensively validate the proposed algorithm in terms of accuracy, based on different metrics and processing time. Comparisons with state-of-the-art algorithms were accomplished in order to evaluate the goodness of the LbL-AD, giving as a result an outstanding performance.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2019.2919911