Fractional Fourier Transform and Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection

Abstract

Most of the algorithms for hyperspectral anomaly detection (AD) are based on the original spectral signatures which may suffer noise contamination. In recent years, some AD algorithms based on deep learning (DL) and tensor have achieved satisfactory results. In this letter, an algorithm using fractional Fourier transform (FrFT) and transferred convolutional neural network based on tensor (FrFTTCNNT) is proposed for hyperspectral AD. First, the test block of each test point in hyperspectral imagery (HSI) is transformed into 1-D vector and a higher dimensional data set with more spatial information is obtained. Furthermore, the higher dimensional data set is dimensionally reduced by principal component analysis (PCA) to remove the redundancy of spectral bands. Then, the lower dimensional data set after PCA is transformed by FrFT and the nonstationary noise in the fractional Fourier domain (FrFD) can be better suppressed which may increase the discrimination between background and targets. Finally, in the FrFD, transferred CNN based on tensor (TCNNT) is employed for the final results. Experiments conducted on three hyperspectral data sets show the superiority of the proposed FrFTTCNNT.