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Recently anomaly detection (AD) has become an important application fortarget detection in hyperspectral remotely sensed images. In many applications,in addition to high accuracy of detection we need a fast and reliable algorithmas well. This paper presents a novel method to improve the performance ofcurrent AD algorithms. The proposed method first calculates Discrete WaveletTransform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then,AD algorithm performs on four bands of "Wavelet transform" matrix which are theapproximation of main image. In this research some benchmark AD algorithmsincluding Local RX, DWRX and DWEST have been implemented on AirborneVisible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets.Experimental results demonstrate significant improvement of runtime in proposedmethod. In addition, this method improves the accuracy of AD algorithms becauseof DWT's power in extracting approximation coefficients of signal, whichcontain the main behaviour of signal, and abandon the redundant information inhyperspectral image data.

Improvement of Anomoly Detection Algorithms in Hyperspectral Images using Discrete Wavelet Transform