Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70165
Title: A Hardware-Friendly Anomaly Detector for Real-Time Applications with Push-Broom Scanners
Authors: Díaz Martín, María 
Guerra Hernández, Raúl Celestino 
López Suárez, Sebastián 
Keywords: Anomaly Detection
Floating-Point Arithmetic
Hyperspectral Imaging
Integer Arithmetic
Orthogonalization, et al
Issue Date: 2019
Journal: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing 
Conference: 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 
Abstract: In this work, we propose a hardware-friendly anomaly detector as a modified version of the LbL-FAD, named HW-LbL-FAD, specifically designed for being executed into parallel hardware devices with limited computational resources. As the original LbL-FAD, the HW-LbL-FAD makes use of the orthogonal projection concept for identifying the subspace in which the background pixels are contained, thus identifying the anomalous pixels as those not totally contained in this subspace. However, the HW-LbL-FAD makes a more efficient use of the Modified Gram-Schmidt orthogonalization process, resulting in a mathematically equivalent algorithm that requires less computational and memory resources. Additionally, the HW-LbL-FAD algorithm can be totally executed using integer arithmetic at different levels of precision that can be adapted for achieving the best relation between detection accuracy and computational burden. The HW-LbL-FAD has been tested in this work using 3 real hyperspectral images typically employed for validating anomaly detection application. These images have been analyzed using both floating-point arithmetic and integer arithmetic with different levels of precision. The obtained results demonstrate the goodness of this proposal.
URI: http://hdl.handle.net/10553/70165
ISSN: 2158-6276
DOI: 10.1109/WHISPERS.2019.8921023
Source: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing [ISSN 2158-6276], v. 2019-September
Appears in Collections:Actas de congresos
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