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Ground to ground, sensor to object viewing perspective presents a major challenge for autonomous window based object detection, since object scales at this viewing perspective cannot be approximated. In this paper, we present a fully autonomous parallel approach to address this challenge. Using hyperspectral (HS) imagery as input, the approach features a random sampling stage, which does not require secondary information (range) about the targets; a parallel process is introduced to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results. The probability of sampling targets by chance within the parallel processes is modeled by the binomial distribution family, which can assist on tradeoff decisions. Since this approach relies on the effectiveness of its core algorithmic detection technique, we also propose a compact test statistic for anomaly detection, which is based on a principle of indirect comparison. This detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly reducing the number of false positives (e.g. transitions of background regions). To capture the influence of parametric changes using both the binomial distribution family and actual HS imagery, we conducted a series of rigid statistical experiments and present the results in this paper.

In this article we describe the end-to-end processing of image Fourier transform spectrometry data taken at Picatinny Arsenal in New Jersey with the long-wave hyperspectral camera manufactured by the Telops company. The first part of the article discusses the processing from raw data to calibrated radiance and emissivity data. Data were taken during several months under different weather conditions every 6 min from a 213 ft-high tower of surrogate tank targets for a project sponsored by the Army Research Laboratory in Adelphi, Maryland. The second part discusses environmental effects such as diurnal and seasonal atmospheric and temperature changes and the effect of cloud cover on the data. To test the effect of environmental conditions, a range-invariant anomaly detection approach is applied to calibrate radiance, brightness temperature, and emissivity data. The data set presented in this article is due to be released publicly so that more detailed studies can be performed not only on the manmade targets but also on the changes of natural background of vegetation and gravel with time of day and seasons.