Abstract
Anomaly detection in hyperspectral images is an important and challenging problem. Most available data sets are unlabeled, and very few are labelled. In this paper, we proposed a lightweight Variational Autoencoder anomaly detector (VAE-AD) for hyperspectral data. VAE is used to learn the background distribution of the image, and thereafter it is used to construct a background representation for each pixel. Further reconstruction error is calculated between the background reconstructed image and the original image used for anomaly detection. A GMM-based post-processing step is used to construct the final detection map. The comparative analysis with five real-world hyperspectral data sets shows that the proposed model achieves better or comparable results with few learning parameters of the model, and with less time.
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Ojha, N., Sinha, I.K., Singh, K.P. (2023). VAE-AD: Unsupervised Variational Autoencoder for Anomaly Detection in Hyperspectral Images. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_11
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DOI: https://doi.org/10.1007/978-981-99-1648-1_11
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