The Dark HORSE 1 (Hyperspectral Overhead Reconnaissance and Surveillance Experiment 1) flight test has demonstrated autonomous, real-time visible hyperspectral detection of military ground targets with real-time cuing of a high-resolution framing camera. An overview of the Dark HORSE 1 hyperspectral sensor system is presented. The system hardware components are described in detail, with an emphasis on the visible hyperspectral sensor and the real-time processor. Descriptions of system software and processing methods are also provided. The recent field experiment in which the Dark HORSE 1 system was employed is described in detail along with an analysis of the collected data. The results evince per-pixel false-alarm rates on the order of 10 Ϫ5 /km 2 , and demonstrate the improved performance obtained by operating two detection algorithms simultaneously.
Multitemporal monitoring of sites using spectral imagery is addressed. A comprehensive architecture is presented for the detection of significant changes in scene composition described at the object level of spatial scale. An object-level scene description is obtained by applying a statistical spectral anomaly detector followed by a competitive region growth object extractor. The competitive region growth algorithm is derived as the solution to an approximate maximum likelihood (ML) image segmentation problem. Gaussian spectral clustering is used to model the scene background. A digital site model is constructed that contains image segmentation maps and extracted object features. Object-level change detection (OLCD) is accomplished by comparing objects extracted from a new image to objects recorded in the site model. A restricted implementation of the architecture is described and tested on long-wave infrared hyperspectral imagery. It is demonstrated that spectral OLCD can eliminate false alarms based on their multitemporal persistence. Incorporating multiple images in the site model is observed to improve OLCD performance.
This article describes a series of mid-IR FT-IR reflectance spectroscopy measurements of hydrocarbon-contaminated wet soils. The eventual goal of this work is the development of an analysis tool suitable for real-time in situ underground measurements where a suitable reference spectrum is not available. Multivariate analysis of the resulting spectral data indicates that the strongly varying wet soil matrix and the absence of a suitable reference spectrum in the field do not render this measurement technique unfeasible as a means of realizing remote in situ chemical detection in wet soils. It was also observed that simultaneous quantification of moisture content and identification of soil composition may be achieved. These results have important applications to in situ site characterization for environmental cleanup and soil characterization for construction planning.
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