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Kernel wavelet-Reed-Xiaoli: an anomaly detection for forward-looking infrared imagery
Asif Mehmood1, Nasser M Nasrabadi
1U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, Maryland 20783, USA. asif.mehmood1@us.army.mil
Applied Optics
|June 16, 2011
Summary
A new kernel wavelet-Reed-Xiaoli (wavelet-RX) algorithm enhances anomaly detection in long-wave infrared imagery. This advanced method outperforms existing techniques by processing data in a high-dimensional feature space.
Area of Science:
- Image processing
- Anomaly detection
- Infrared imaging
Background:
- Long-wave infrared (LWIR) imagery presents challenges for anomaly detection.
- Existing methods like Reed-Xiaoli (RX) anomaly detection and wavelet transform have limitations.
- Need for advanced techniques to improve detection accuracy in complex LWIR data.
Purpose of the Study:
- To introduce a novel kernel wavelet-based anomaly detection technique for LWIR imagery.
- To extend the wavelet-RX algorithm into a high-dimensional feature space using kernel methods.
- To evaluate the performance of the proposed kernel wavelet-RX algorithm against existing methods.
Main Methods:
- Application of a two-dimensional wavelet transform to decompose LWIR images into subbands.
- Concatenation of high-energy subbands to form a subband-image cube.
- Implementation of the kernel RX algorithm on the subband-image cube for anomaly detection.
Main Results:
- The proposed kernel wavelet-RX algorithm demonstrated superior performance in detecting anomalies in LWIR imagery.
- Comparative analysis showed significant improvements over the standard wavelet-RX and Constant False Alarm Rate (CFAR) algorithms.
- Receiver Operating Characteristic (ROC) plots confirmed the enhanced detection capabilities of the kernel wavelet-RX method.
Conclusions:
- The kernel wavelet-RX algorithm offers a powerful and effective approach for anomaly detection in LWIR forward-looking infrared imagery.
- Kernelization of the wavelet-RX algorithm enables processing in high-dimensional feature spaces, leading to improved detection accuracy.
- This technique provides a valuable advancement for identifying subtle anomalies in complex infrared datasets.
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