Proceedings Article10.1109/ICIP.2004.1421827
Hyperspectral anomaly detection using kernel RX-algorithm
Heesung Kwon,Nasser M. Nasrabadi +1 more
- 24 Oct 2004
- Vol. 5, pp 3331-3334
15
TL;DR: A nonlinear version of the well-known anomaly detection method, referred to as the RX-algorithm, is presented by extending this algorithm in a feature space associated with the original input space via a certain nonlinear mapping function.
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Abstract: In this paper we present a nonlinear version of the well-known anomaly detection method, referred to as the RX-algorithm, by extending this algorithm in a feature space associated with the original input space via a certain nonlinear mapping function. An expression for the nonlinear form of the RX-algorithm is derived which is basically intractable mainly due to the high dimensionality of the feature space. We convert the nonlinear RX expression into kernels, which implicitly compute dot products in the nonlinear domain. The proposed kernel RX-algorithm is applied to hyperspectral images for anomaly detection. Improved performance of the kernel RX over the conventional RX is shown for the HYDICE (hyperspectral digital imagery collection experiment) images tested.
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Citations
Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data
TL;DR: Experimental results indicate that the proposed optimizations can significantly improve the performance of the considered algorithms without reducing their anomaly detection accuracy.
319
An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition
P Wt Yuen,Mark A. Richardson +1 more
TL;DR: The pros and cons of the various HSI system configurations are outlined, with particular emphasis on two of the most commonly deployed spectrograph techniques, namely, the dispersive system and the narrow-band tuning filter system.
284
Hyperspectral anomaly detection by local joint subspace process and support vector machine
TL;DR: The proposed hyperspectral anomaly detection algorithm based on local joint subspace process and support vector machine (SVM) has shown a superior performance on both synthetic and real-world datasets.
48
Anomaly detection based on a parallel kernel RX algorithm for multicore platforms
TL;DR: The proposed approach makes use of linear algebra libraries and further develops a parallel implementation optimized for multi-core platforms, which is a well known, inexpensive and widely available high performance computing technology.
Spectral anomaly detection in deep shadows
TL;DR: A novel hyperspectral anomaly detection algorithm that adapts the dimensionality of the spectral detection subspace to multiple illumination levels is described and results obtained for objects located in deep shadows and light-shadow transition areas suggest superiority of the novel algorithm over standard subspace RX detection.
12
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