![llustration d'une onde électromagnétique monochromatique, plane (source : [18]). Cette onde est caractérisée par sa période (T ), sa fréquence (f ), sa célérité (v) et sa longueur d'onde (λ).](https://www.researchgate.net/profile/Ramzi-Idoughi/publication/286947857/figure/fig3/AS:614358874873875@1523485978443/Illustration-dune-onde-electromagnetique-monochromatique-plane-source-18-Cette_Q320.jpg)
![llustration des différentes fenêtres atmosphériques dans le domaine spectral [0.4 − 13 µm]. VIS : domaine du visible, SWIR : Short-Wavelength InfraRed, MWIR : Mid-Wavelength InfraRed, LWIR : Long-Wavelength InfraRed. Transmission atmosphérique au nadir pour une altitude de 3 km. Cette transmission atmosphérique a été calculée par l'outil MODTRAN (cf. §2.2.5).](https://www.researchgate.net/profile/Ramzi-Idoughi/publication/286947857/figure/fig2/AS:614358874861568@1523485978036/Illustration-des-differentes-fenetres-atmospheriques-dans-le-domaine-spectral-04-13_Q320.jpg)
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Gaseous pollutants characterization using airborne hyperspectral measurements at high spatial resolution
Abstract and Figures
Les émissions atmosphériques constitue un enjeu majeur pour la société, à la fois pour les problématiques santé - qualité de l’air (maladies respiratoires, allergies, ...) et pour les problématiques liées au réchauffement climatique et aux gaz à effet de serre. Les sources anthropiques, industrielles en particulier, émettent dans l’atmosphère gaz et aérosols qui jouent un rôle important dans les échanges atmosphériques. Néanmoins leur suivi à haute résolution spatiale reste peu précis, en raison des résolutions rencontrées pour les senseurs spatiaux. Les développements techniques récents des capteurs hyperspectraux aéroportés permettent d’améliorer la caractérisation des panaches. Lors de nos travaux nous avons développé un nouvel outil pour la détection et la caractérisation des panaches de gaz. Ainsi, une cartographie 3D des différentes concentrations est obtenue. Cet outil est ensuite validé sur des images synthétiques et sur des acquisitions aéroportées de scènes industrielles.
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Anthropogenic sources, especially industrial, emit into the atmosphere gases and aerosols, which play an important role in atmospheric exchanges. However industrial emissions are poorly estimated as it needs a sensor with a high spatial resolution with a good spectral resolution. The use of the new hyperspectral airborne image sensors in the thermal infrared range opens the way to new development to improve plume remote sensing.
The thermal domain (MWIR-LWIR) spectrometry has been employed for decades to detect and estimate gaseous pollutants. Indeed, the majority of gas plume have their signature in the thermal domain. However, the impact of plume’s temperature and the heterogeneity of ground properties make the gas characterization more difficult.
Existing methods have several limitations: (i) the heterogeneous environment impact on their performances; (ii) spatial and vertical extent of the plume is not taken into account.
In this work, a new method for characterizing gas plumes is presented to overcome such limitations. This method is based on an accurate non linear formalism of cloud gas radiative impact. It includes: (i) a ground classification of the scene, in order to take account the soil's heterogeneity and spectral behavior; (ii) and an optimal estimation formalism taking into account constraint on spatial and vertical plume structure.
Keywords: Hyperspectral, thermal infra-red, plume, gas, detection, characterization.
We propose an algorithm for standoff quantification of chemical vapor plumes from hyperspectral imagery. The approach is based on the observation that the quantification problem can be easily solved in each pixel with the use of just a single spectral band if the radiance of the pixel in the absence of the plume is known. This plume-absent radiance may, in turn, be recovered from the radiance of the subset of spectral bands in which the gas species is transparent. This “selected-band” algorithm is most effective when applied to gases with narrow spectral features, and are therefore transparent over many bands. We also demonstrate an iterative version that expands the range of applicability. Simulations show that the new algorithm attains the accuracy of existing nonlinear algorithms, while its computational efficiency is comparable to that of linear algorithms.
Remote sensing has become a widely used tool in ecology. Examples of ecological applications that use remote sensing include species conservation efforts such as GAP analysis Scott et al. 1993, land cover and land use change monitoring (Skole and Tucker 1993; DeFries and Townshend 1994), and estimation of ecosystem carbon assimilation rates and net primary production (Prince 1991). At biome to global scales, it has also been demonstrated that the utility of remote sensing for monitoring ecosystem dynamics at time scales is commensurate with global change processes (Braswell et al. 1997; Myneni et al. 1997). Developments in remote sensing technologies, including airborne radar, video imaging systems, and satellite instruments with high spatial and spectral resolution show substantial promise for ecological studies. Further, even though this chapter focuses on optical remote sensing, new technologies (e.g., radar, laser altimeter, and lidar systems, which provide detailed information regarding topography and vegetation structure in three dimensions) suggest that the use of remote sensing by ecologists is likely to increase in the future.
This report compares two Gaussian atmospheric dispersion models, commonly used in France for assessing
population exposure to the releases of pollutants from point sources: ADMS and ARIA impacts.
The working method was to consider the point of view of a model user. Therefore, the comparison was based upon
a real situation: the dispersion of dioxins and furans released from a municipal solid waste incinerator, and the
analysis of soils in its neighbourhood.
The study shows that the differences between the results of the two atmospheric dispersion models can be explained
in many ways. The differences in calculation equations contribute to results variations, in particular deposit
results, the main population exposure pathway to organic pollutants. Another significant explanation lies in the
model user’s interface which conditions its use. Finally, the uncertainty of input parameters, especially concerning
dioxins and furans, has a major impact on the observed discrepancies between the results of different models.
Comparing modelling results to soil measurement data enables to balance the uncertainties associated with
modelling compared to those associated with measurements. Such comparisons trigger the need for increased
knowledge on the initial state of the environment prior to building any industrial facility, in order to identify the
exogenous elements to the studied source.
The report also proposes guidelines for the users of both models
Many military and civilian applications depend on the ability to remotely sense chemical agent (CA) clouds, from detecting small but lethal concentrations of chemical warfare agents (CWAs) to mapping plumes in the aftermath of natural disasters. Hyperspectral imaging sensors, in the long-wave infrared (IR) (LWIR) part of the spectrum, are particularly well suited for these gas-sensing tasks. Their wide fields of view allow many square kilometers to be imaged almost simultaneously, and their high spectral resolutions often allow the detection of chemical clouds even when they are optically thin (or nearly transparent).
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
The passive remote chemical plume quantication problem may be approached from multiple aspects, corresponding to a variety of physical eects that may be exploited. Accordingly, a diversity of statistical quantication algorithms has been proposed in the literature. The ultimate performance and algorithmic complexity of each is in uenced by the assumptions made about the scene, which may include the presence of ancillary measurements or particular background / plume features that may or may not be present. In this paper, we evaluate and compare a number of quantication algorithms that span a variety of such assumptions.