Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

By Heesung Kwon, Nasser M. Nasrabadi. IEEE Transactions on Geoscience and Remote Sensing, 2005

804 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation (2016, methodology): “Moreover, kernel-based methods such as kernel-RX [10], [11] and support vector data description [12], [13] were proposed based on the kernel theory for extending the original space to a higher dimensional feature space.”
Outlier Detection (2008, background): “This is closely related to the standard supervised learning problem except that all the training samples have the same normal label.”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “Along with some already mentioned anomaly detectors, such as Reed-Xiaoli (RX) [79] and subspace-based [84] detectors, others were highlighted as emerging approaches, more specifically, Kernel RX [85]—non-linear version of RX detector to deal with the lack of Gaussian distribution behavior of clutter”
Collaborative Representation for Hyperspectral Anomaly Detection (2015, neutral): “(13)”
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters (2017, methodology): “For example, a kernel RX algorithm is proposed in [8], which takes into account the higher order relationships between the spectral bands by mapping the hyperspectral data into a high-dimensional feature space that could be potentially of infinite dimension.”