Efficient Nonlinear RX Anomaly Detectors

Creative Commons License

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

IEEE Geoscience and Remote Sensing Letters, cilt.18, sa.2, ss.231-235, 2021 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/lgrs.2020.2970582
  • Dergi Adı: IEEE Geoscience and Remote Sensing Letters
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.231-235
  • Anahtar Kelimeler: 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
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet

Özet

© 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.