Image spectrometry

B) SCATTERING AND RECOILING IMAGING SPECTROMETRY (SARIS)—BLOCKING CONE BASED EXPERIMENT... 

Bennett, C. L., Carter, M., Fields, D. and Hernandez, J. (1937) Imaging Fourier transform spectrometer. In Proceedings of the Imaging Spectrometry of the Terrestrial Environment, 14-15 April 1993, Orlando, FL, SPIE 191. 

Vane, G. and Goetz, A. F. H. (1993) Terrestrial imaging spectrometry current status, future trends. Remote Sensing Environm. 44, 117-26. 

Thus far, we have reviewed basic theories and experimental techniques of Raman spectroscopy. In this chapter we shall discuss the principles, experimental design and typical applications of Raman spectroscopy that require special treatments. These include high pressure Raman spectroscopy, Raman microscopy, surface-enhanced Raman spectroscopy, Raman spectroelectro-chemistry, time-resolved Raman spectroscopy, matrix-isolation Raman spectroscopy, two-dimensional correlation Raman spectroscopy, Raman imaging spectrometry and non-linear Raman spectroscopy. The applications of Raman spectroscopy discussed in this chapter are brief in nature and are shown to illustrate the various techniques. Later chapters are devoted to a more extensive discussion of Raman applications to indicate the breadth and usefulness of the Raman technique. 

Surface-Enhanced Raman Spectroscopy (SERS) Raman Spectroelectrochemistry Time-Resolved Raman (TR-) Spectroscopy Matrix-Isolation Raman Spectroscopy 2D Correlation Raman Spectroscopy Raman Imaging Spectrometry Nonlinear Raman Spectroscopy References... 

Ifarraguerri, A, Exploitation of imaging spectrometry data for chemical cloud detection, identification and mapping. Proceedings of the 1997 ERDEC Conference on Chemical Defense Research, Special Publication ERDEC-SP-063,401-406 (1998)... 

Kallio, K., Koponen, S. and Pulbainen, J. (2003) Feasibility of airborne imaging spectrometry for lake monitoring - a case study of spatial chlorophyll a distribution in two meso-eutrophic lakes. International Journal of Remote Sensing, 24, pp. 3771-3790. 

Asner, G. P., Wessman, C. A., and Schiinel, D. S. (1998b). Fleterogeneity of savanna canopy. structure and function from imaging spectrometry and inverse modeling. Ecol. Appl. 8, 1022-1036. 

Curran, P. J. (1994) Imaging Spectrometry. Pragresi in Physical Geography 18,247-... 

Earth Observing Systems Reports, Vol. lie. High Resolution Imaging Spectrometry (NASA, Washington, DC 1986)... 

PJ Treado, IW Levin, EN Lewis. High-fidelity Raman imaging spectrometry A rapid method using an acousto-optic tunable filter. Appl Spectrosc 46 1211-1216, 1992. 

Bordas C, Paulig FH, Helm H, Huestis DL (1996) Photoelectron imaging spectrometry Principle and inversion method. Review of Scientific Instruments 67 2257 2268. 

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). 

In this entry we provided a brief description of the different aspects of the use of hyperspectral data in urban environment. In the introduction we presented a brief overview of imaging spectrometry in urban areas. The discussion continued with a description of the characteristics of the hyperspectral sensors and the problems related to the processing of hyperspectral images. We illustrated then the different techniques for the assessment of building vulnerability through the use of hyperspectral data. 

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