Mapping imaging spectrometer

Figure 13. Modified Velocity Map Imaging spectrometer showing the double einzel lens, L, Li, and 5-eV kinetic energy initially transverse trajectories from an extended source volume with Vjgp = 3000 V, Vext = 0.695 x Vjep, and Vl = Vli = 1000 V. Taken with permission from Ref. [102]. Copyright (c) 2005, American Institute of Physics.

Figure 14.8. Hybrid imaging/mapping imaging spectrometer incorporating a rapid-scan interferometer and a linear array detector. (Reproduced from [21], by permission of CRC Press copyright 2003.)...

In the x-ray portion of the spectmm, scientific CCDs have been utilized as imaging spectrometers for astronomical mapping of the sun (45), galactic diffuse x-ray background (46), and other x-ray sources. Additionally, scientific CCDs designed for x-ray detection are also used in the fields of x-ray diffraction, materials analysis, medicine, and dentistry. CCD focal planes designed for infrared photon detection have also been demonstrated in InSb (47) and HgCdTe (48) but are not available commercially. 

The VIRTIS apparatus (Visible Infrared Thermal Imaging Spectrometer) on board can observe the atmosphere and the cloud layers at various depths (on both the day and the night side of the planet). VIRTIS has also provided data for the first temperature map of the hot Venusian surface. These data have led to the identification of hot spots and thus provided evidence for possible volcanic activity (www.esa.int/specials/venusexpress). 

The technique of obtaining images of specific elements, the so-called elemental maps, is another important application in EELS analysis. Energy filtered TEM (EFTEM) images can be obtained with a so-called imaging spectrometer. However, the images are not directly interpretable as chemical maps because of the nonspecific background in the EELS spectrum. 

In general, several tools have been developed to match reference spectra with those measured by an imaging spectrometer. The most common approach is based on the use of standard supervised classification techniques, where known spectra are used to determine the statistical properties of each class based on spectral characteristics. Examples of supervised classification approaches applied to hyperspectral data are described in McKeown et al. (1999) and Roessner et al. (2001), where the maximum likelihood classifier (MLC) was applied to map urban land cover. Other techniques are based on the use of support vector machines (SVM) (Melgani and Bruzzone 2004) and neural networks (NNs) (Licciardi et al. 2009, 2012). Other approaches have been designed explicitly for the analysis of imaging spectrometry data, such as the Spectral Angle Mapper (SAM Kruse et al. 1993). 

Tel Aviv, Israel. Data acquired by the Digital Airborne Imaging Spectrometer (DAIS) have been used by Roessner et al. (2001) to obtain a map of urban materials in the city of Dresden, Germany. In this study, a maximum likelihood classifier has been used to derive a first map of pure spectral features and then used these feamres to unmix the other spectra. 

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