Sparse Autoencoder Based Hyperspectral Anomaly Detection with the Singular Spectrum Analysis Based Spectral Denoising

As an effective tool for monitoring surface irregularities in remote sensing, hyperspectral anomaly detection (HAD) has garnered increasing attention. However, how to improve the detection accuracy remains a formidable challenge, due mainly to the noise and variations in the spectral domain, especially when there is lack of the labelled data for training. To tackle these difficulties, a novel unsupervised HAD method is proposed. First, 1-D Singular Spectrum Analysis (SSA) is employed to eliminate outliers in the spectral domain. Second, the SSA-smoothed hypercube undergoes a sparse autoencoder for background reconstruction, where the reconstruction error is used to extract anomalous pixels. Finally, the RX algorithm is employed to segment anomalous pixels from the background. Comprehensive experiments on four publicly available datasets have validated the superior performance of our method in effectively enhancing the separability between anomaly pixels and their respective backgrounds, outperforming a few state-of-the-art methods, particularly in terms of the detection accuracy.