Anomaly detection is an important task for remotely sensed hyperspectral data exploitation. One of the most
widely used and successful algorithms for anomaly detection in hyperspectral images is the Reed-Xiaoli (RX)
algorithm. Despite its wide acceptance and high computational complexity when applied to real hyperspectral
scenes, few documented parallel implementations of this algorithm exist, in particular for multi-core processors.
The advantage of multi-core platforms over other specialized parallel architectures is that they are a low-power,
inexpensive, widely available and well-known technology. A critical issue in the parallel implementation of RX
is the sample covariance matrix calculation, which can be approached in global or local fashion. This aspect is
crucial for the RX implementation since the consideration of a local or global strategy for the computation of
the sample covariance matrix is expected to affect both the scalability of the parallel solution and the anomaly
detection results. In this paper, we develop new parallel implementations of the RX in multi-core processors and
specifically investigate the impact of different data partitioning strategies when parallelizing its computations.
For this purpose, we consider both global and local data partitioning strategies in the spatial domain of the
scene, and further analyze their scalability in different multi-core platforms. The numerical effectiveness of the
considered solutions is evaluated using receiver operating characteristics (ROC) curves, analyzing their capacity
to detect thermal hot spots (anomalies) in hyperspectral data collected by the NASA's Airborne Visible Infra-
Red Imaging Spectrometer system over the World Trade Center in New York, five days after the terrorist attacks
of September 11th, 2001.