In this paper, we present a variation on the LRX (Local RX) algorithm for detecting anomalies in multi-temporal images.
Our algorithm assigns a relative weight to the Mahalanobis distance according to the number of times it appears in an
image. Standard transitions between pixels are therefore not viewed as anomalous; unusual transitions are assigned
proportionally higher weights. Experimental results using our proposed algorithm vs previous algorithms on multitemporal
datasets show a significant improvement.
I. Dayan, S. Maman, D. G. Blumberg, and S. Rotman, "Multi-temporal anomaly detection technique," Proc. SPIE 9987, Electro-Optical and Infrared Systems: Technology and Applications XIII, 99870G (Presented at SPIE Security + Defence: September 28, 2016; Published: 21 October 2016); https://doi.org/10.1117/12.2239530.
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