Comparison Among Some Anomaly Detection Approaches for Hyperspectral Imagery

Comparison Among Some Anomaly Detection Approaches for Hyperspectral Imagery

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Abstract

Comparison Among Some Anomaly Detection Approaches for Hyperspectral Imagery

S.R.Soofbaf

K.N.T.University of Tehran,
Iran
Email: [email protected]

H.Fahimnejad
Ms
Student of Remote Sensing
K.N.T.University of Tehran
Email: [email protected]

M. J. Valadan Zoej
Assistant Professor
Department of Remote Sensing
K.N.T.University
K.N.T.University of Tehran
Email: [email protected]

B. Mojarad
Phd.student of Photogrammetry & Remote Sensing
K.N.T.University of Tehran
Email: [email protected]

Nowadays the use of hyperspectral imagery, specifically automatic target detection algorithms for these images is a relatively exciting area of research.
An important challenge of hyperspectral target detection is to detect small targets without any prior knowledge, particularly when the interested targets are insignificant with low probabilities of occurrence.The specific characteristic of anomaly detection is that it does not require atmospheric correction and signature libraries. Recently, several useful applications of anomaly detection approaches have been developed in remote sensing.
With this in mind, in this paper some anomaly detectors such as RX-based anomaly detectors, Gauss-Markov random field (GMRF) model,Combine Fisher Test model(CFT),as well as adaptive anomaly detectors,are compared. Finally the most efficient method is proposed for implementation in a planned software system.