Cluster versus GPU implementation of an Orthogonal Target Detection Algorithm for Remotely Sensed Hyperspectral Images

Remotely sensed hyperspectral imaging instruments provide high-dimensional data containing rich information in both the spatial and the spectral domain. In many surveillance applications, detecting objects (targets) is a very important task. In particular, algorithms for detecting (moving or static) targets, or targets that could expand their size (such as propagating fires) often require timely responses for swift decisions that depend upon high computing performance of algorithm analysis. In this paper, we develop parallel versions of a target detection algorithm based on orthogonal subspace projections. The parallel implementations are tested in two types of parallel computing architectures: a massively parallel cluster of computers called Thunderhead and available at NASA’s Goddard Space Flight Center in Maryland, and a commodity graphics processing unit (GPU) of NVidia GeForce GTX 275 type. While the cluster-based implementation reveals itself as appealing for information extraction from remote sensing data already transmitted to Earth, the GPU implementation allows us to perform near real-time anomaly detection in hyperspectral scenes, with speedups over 50x with regards to a highly optimized serial version. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the attacks that collapsed the two main towers in the WTC complex.