A tutorial overview of anomaly detection in hyperspectral images

By Stefania Matteoli, Marco Diani, Giovanni Corsini. IEEE Aerospace and Electronic Systems Magazine, 2010

459 citations recorded by the source corpus.

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “To conclude this subsection devoted to the targets’ detection within hyperspectral data, there are other works that are worthy of being consulted [54] and some related references [71,97,98] that might complete and update some of the previously addressed approaches as, for example, anomaly detection ”
Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature (2014, methodology): “The hyperspectral anomaly detector used in our approach is named as “Reed–Xiaoli” (RX) [36], an unsupervised benchmark algorithm for hyperspectral anomaly detection [18], [29], [37], [38].”
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, background): “the results are often unstable when the local background contains large disturbances or multiple classes [10], [11].”
Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification (2018, background): “2772836 Hyperspectral data, with its rich spectral information, have been playing a key role in remote sensed data analysis [13], [14] such as land cover classification [15]–[17] and anomaly detection [18].”