Automatic target and anomaly detection are considered very important tasks for hyperspectral data exploitation.
These techniques are now routinely applied in many application domains, including defence and intelligence,
public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for
swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent
explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation
of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast
anomaly and target detection in hyperspectral data sets already transmitted to Earth. However, these systems
are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power
integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time,
i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of
commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge
the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we describe several
new GPU-based implementations of target and anomaly detection algorithms for hyperspectral data exploitation.
The parallel algorithms are implemented on latest-generation Tesla C1060 GPU architectures, and quantitatively
evaluated using hyperspectral data collected by NASA's AVIRIS system over the World Trade Center (WTC)
in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex.