Abstract
The basic knowledge about the differences between multi- and hyperspectral data is provided and the potential of hyperspectral image analysis is highlighted. Relevant pre-processing steps and different ways to analyze hyperspectral data are presented. The chapter closes with a short outlook on expected developments with relevance for urban applications.
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References
ASD – Analytical Spectral Devices (2004) Spectroradiometers – FieldSpec3. http://www.asdi.com/products-fs3.asp. Accessed 11 Mar 2009
ASTER (1998) ASTER spectral library. http://speclib.jpl.nasa.gov/search-1/manmade. Accessed 11 Mar 2009
Ben-Dor E (2001) Imaging spectrometry for urban applications. Kluwer, Dordrecht/Boston/London, pp 243–281
Buckingham R, Staenz K (2008) Review of current and planned civilian space hyperspectral sensors for EO. Can J Remote Sens 34:187–197
Gao BC, Goetz A (1990) Column atmospheric water vapour and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res 95:3549–3564
Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing science. Science 228:1147–1153
Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26(1):65–74
Heiden U, Rößner S, Segl K (2001) Potential of hyperspectral HyMap data for material oriented identification of urban surfaces. In: Proceedings of remote sensing of urban areas, Regensburg, Germany, pp 69–77
Herold M, Roberts DA, Gardner ME, Dennison PE (2004) Spectrometry for urban area remote sensing – Development and analysis of a spectral library from 350 to 2400 nm. Remote Sens Environ 91:304–319
Hill J, Mehl W (2003) Geo- und radiometrische Aufbereitung multi- und hyperspektraler Daten zur Erzeugung langjähriger kalibrierter Zeitreihen. Photogrammetrie-Fernerkundung-Geoinformation 2003(1):7–14
Hostert P, Damm A (2003) Sensitivity analysis of multi-source spectra from an urban environment. 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, Germany, pp 215–219
Lehmann F, Bucher T, Hese S, Hoffmann A, Mayer S, Oschütz F, Zhang Y (1998) Data fusion of HyMap hyperspectral with HRSC-A multispectral stereo and DTM data. 1st EARSeL Workshop Imaging Spectroscopy, Zürich, Switzerland, pp 105–117
Phinn S, Stanford M, Scarth P, Murray AT, Shyy T (2002) Monitoring the composition and form of urban environments based on the vegetation – impervious surface – soil (VIS) model by sub-pixel analysis techniques. Int J Remote Sens 23(20):4131–4153
DLR – Deutsches Zentrum für Luft- und Raumfahrt/German Aerospace Centre (2003) The Quicklooks of HyEurope 2003 http://www.op.dlr.de/dais/hyeurope2003/hyeurope2003_ql.html. Accessed 11 Mar 2009
Richter R, Stanford D (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. Int J Remote Sens 23(13):2631–2649
Ridd MK (1995) Exploring a V–I–S (vegetation–impervious surface–soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16:2165–2185
Schiefer S, Hostert P, Damm A (2006) Correcting brightness gradients in hyperspectral data from urban areas. Remote Sens Environ 101:25–37
Schläpfer D, Richter R, Damm A (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 1: Parametric orthorectification. Int J Remote Sens 23(13):2609–2630
Schlerf M, Atzberger CG, Hill J (2005) Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ 95(2):177–194
Segl K, Rößner S, Heiden U (2000) Differentiation of urban surfaces based on hyperspectral image data and a multi-technique approach. In: Proceedings of the IEEE IGARSS 2000, Honolulu, pp 1600–1602
Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens Environ 88:170–186
van der Linden S, Janz A, Waske B, Eiden M, Hostert P (2007) Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines. J Appl Remote Sens 1. doi:10.1117/1.2813466
van der Linden S, Hostert P (2009) The influence or urban surface structures on the accuracy of impervious area maps from airborne hyperspectral data. Remote Sens Environ 113:2298–2305
Wilson IJ, Cocks TD (2003) Development of the Airborne Reflective/Emissive Spectrometer (ARES) – a progress report. In: Proceedings of the 3rd EARSeL Workshop Imaging Spectroscopy, Herrsching, Germany, pp 50–55
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Hostert, P. (2010). Processing Techniques for Hyperspectral Data. In: Rashed, T., Jürgens, C. (eds) Remote Sensing of Urban and Suburban Areas. Remote Sensing and Digital Image Processing, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4385-7_9
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DOI: https://doi.org/10.1007/978-1-4020-4385-7_9
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