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Anomaly detection algorithms for hyperspectral imagery

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Anomaly detection algorithms for hyperspectral imagery

Seyyed Reza Soofbaf
Ms student of Remote Sensing
K.N.T.University of Tehran,
Email: [email protected]

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

Barat Mojaradi
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( MRX,NRX,CRX,RX-UTD), Combined Fisher Test (CFT) model, as well as adaptive anomaly detectors such Nested Spatial Window-Based approach(NSW) and dual window-based eigen separation transform (DWEST) ,are compared. Finally the most efficient method is proposed for implementation in a planned software system.