Efficient Nonlinear RX Anomaly Detectors

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Padron Hidalgo J. A., Perez-Suay A., NAR F., Camps-Valls G.

IEEE Geoscience and Remote Sensing Letters, vol.18, no.2, pp.231-235, 2021 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.1109/lgrs.2020.2970582
  • Journal Name: IEEE Geoscience and Remote Sensing Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.231-235
  • Keywords: Kernel, Detectors, Two dimensional displays, Computational efficiency, Anomaly detection, Approximation algorithms, Standards, Anomaly detection (AD), hyperspectral, kernel methods, low-rank approximation, nonlinear methods, Nystr&#246, m method, randomization, randomized feature maps, Reed&#8211, Xiaoli (RX) detector
  • Open Archive Collection: AVESIS Open Access Collection
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

Abstract

© 2004-2012 IEEE.Current anomaly detection (AD) 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 (KRX) method for AD by approximating the kernel function with either the data-independent random Fourier features or the data-dependent basis with the Nyström 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 to (or outperform) the standard KRX algorithm thanks to their implicit regularization effect. Last but not least, the Nyström approach has an improved power of detection.