Detection of gaseous plumes in IR hyperspectral images-performance analysis

The emergence of IR hyperspectral sensing technology in recent years has enabled its use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging, which combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform IR, provides significant advantages. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background, or any other temporal information. In this paper, we introduce a novel approach aimed for detection and identification of gaseous plumes in IR hyperspectral imagery, using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on actual IR hyperspectral images of the release of several atmospheric tracers. The performance analysis of the proposed algorithm and its detection thresholds is presented. The technique of hyperspectral IR imagery can be used for other applications, such as mapping of the propagation of atmospheric tracers in confined spaces as well as in cases of very low winds.