Eitan Hirsch

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. 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. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.

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.

DISCRIMINATION BETWEEN NATURAL DENSE DUST CLOUDS WITH IR SPECTRAL MEASUREMENTS

Heavy loads of aerosols in the air have considerable health effects in individuals who suffer from chronic breathing difficulties. This problem is more acute in the Middle-East, where dust storms in winter and spring transverse from the neighboring deserts into dense populated areas. Discrimination between the dust types and association with their source can assist in assessment of the expected health effects. A method is introduced to characterize the properties of dense dust clouds with passive IR spectral measurements. First, we introduce a model based on the solution of the appropriate radiative transfer equations. Model predictions are presented and discussed. Actual field measurements of silicone-oil aerosol clouds with an IR spectro-radiometer are analyzed and compared with the theoretical model predictions. Silicone-oil aerosol clouds have been used instead of dust in our research, since they are composed of one compound in the form of spherical droplets and their release is easily controlled and repetitive. Both the theoretical model and the experimental results clearly show that discrimination between different dust types using IR spectral measurements is feasible. The dependence of this technique on measurement conditions, its limitations, and the future work needed for its practical application of this technique is discussed.

Remote detection of SF6 plumes in a stable boundary layer

Dissemination of SF6 and tracking its dispersion in the atmosphere is a well-known technique used to predict how pollutant affects the environment. Remote thermal imaging of the atmospheric tracer plume is one of the methods employed to detect and track its dispersion. However, remote detection of SF6 plumes in a stable boundary layer of the atmosphere (SBL) with a multispectral infrared sensor is a challenging task. At SBL conditions the tracer cloud tends to disperse very slowly and therefore its temporal signature is well mixed with the natural temperature variations over the background scene. Furthermore, SBL conditions are frequent during nighttime when the thermal contrast between the air and the background scene is very low. In this article we propose an efficient method to overcome these difficulties. The local temperature variance of the clean background is compared to the variance measured at the same position during the cloud presence in the field of view. The local temperature variance is modified by passage of radiation through the absorbing cloud. The distinctive spectral signature of the atmospheric tracer is expressed in the relative strength of the different spectral band of the IR sensor. The proposed technique is demonstrated with actual data collected during field test in an urban area. Urban background is particularly suitable for applying this method due to its inherent large thermal variance consisted of buildings, streets, parks etc. We demonstrate the usefulness of this detection method for accurate quantitative estimation of the tracer cloud density and its form.