Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising.

Li, Yinhe; Ren, Jinchang; Gao, Zhi; Sun, Genyun

Abstract

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.

Citation

LI, Y., REN, J., GAO, Z. and SUN, G. 2024. Sparse autoencoder based hyperspectral anomaly detection with the singular spectrum analysis based spectral denoising. In Proceedings of the 2024 IEEE International geoscience and remote sensing symposium (IGARSS 2024), Athens, Greece, 7-12 July 2024. Piscataway: IEEE [online], pages 9210-9213. Available from: https://doi.org/10.1109/igarss53475.2024.10641314

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE International geoscience and remote sensing symposium (IGARSS 2024)
• Start Date: Jul 7, 2024
• End Date: Jul 12, 2024
• Acceptance Date: Mar 15, 2024
• Online Publication Date: Jul 7, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Sep 12, 2024
• Publicly Available Date: Oct 3, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 9210-9213
• Series ISSN: 2153-7003
• DOI: https://doi.org/10.1109/igarss53475.2024.10641314
• Keywords: Hyperspectral images; Anomaly detection; Singular spectrum analysis; Sparse autoencoder; RX
• Public URL: https://rgu-repository.worktribe.com/output/2474806