6533b858fe1ef96bd12b5ae2
RESEARCH PRODUCT
Efficient Nonlinear RX Anomaly Detectors
Gustau Camps-vallsAdrian Perez-suayFatih NarJose A. Padron Hidalgosubject
FOS: Computer and information sciencesComputer Science - Machine LearningBasis (linear algebra)Computer scienceComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesApproximation algorithmHyperspectral imaging02 engineering and technologyElectrical Engineering and Systems Science - Image and Video ProcessingGeotechnical Engineering and Engineering GeologyRegularization (mathematics)Machine Learning (cs.LG)Nonlinear systemKernel (linear algebra)Kernel (statistics)FOS: Electrical engineering electronic engineering information engineeringAnomaly detectionElectrical and Electronic EngineeringAnomaly (physics)Algorithm021101 geological & geomatics engineeringdescription
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks to their implicit regularization effect. Last but not least, the Nystr\"om approach has an improved power of detection.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 | IEEE Geoscience and Remote Sensing Letters |