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.
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