Detection algorithms for hyperspectral imaging applications

By Dimitris G. Manolakis, Gary A. Shaw. IEEE Signal Processing Magazine, 2002

1,243 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Media Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/48b9254f150ec91c9363301556e384f9afc996d7

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Citations in the corpus

Sparse Representation for Target Detection in Hyperspectral Imagery (2011, background): “HSI has found many applications in various fields such as military [2]–[4], agriculture [5], [6], and mineralogy [7].”
Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery (2005, methodology): “This stage can be achieved if the spectral signature of the target is known which can be obtained from a spectral library or a spectral matched filter designed from a set of training data [1], [5].”
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, methodology): “Experiment III (Robustness Analysis to Illumination Variability): Parameter τ is Beta distributed with β2 = 1 and β1 in the interval [2], [28].”
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation (2016, methodology): “For numerical comparison, the receiver operating characteristic (ROC) curve is constructed, which acts as the classic comparison measurement for different detection methods [2], [34].”
Hyperspectral Band Selection: A Review (2019, background): “, biophysical parameter retrieval [3], [4], fine classification of complex environments [5], [6], small target detection [7], [8], and so forth.”
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, background): “More recently, hyperspectral (HS) imaging acquiring a scene in several hundreds of contiguous spectral bands has opened a new range of relevant applications such as target detection [6] and spectral unmixing [7].”
Parametric Rao Test for Multichannel Adaptive Signal Detection (2007, methodology): “Interestingly, the parametric Rao test takes a form identical to that of the recently introduced parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) in airborne surveillance radar systems and other similar applications.”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “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, background): “Hyperspectral images are images with a set of bands or channels [28].”
A Physical Model of Human Skin and its Application for Search and Rescue (2012, background): “Hyperspectral sensors provide a great deal of spectral granularity and a potential means for improved detection and classification of select materials [49].”
Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images (2008, methodology): “show how they work in classification experiments [14].”
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters (2017, neutral): “importance in military and civilian applications [4]–[6].”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “Hyperspectral imagery has been used in reconnaissance and surveillance applications where targets of interest are detected and identified [7], [122].”