In this study a learning urban image spectral archive (LUISA) has been developed, that overcomes the issue of an incomplete spectral library and can be used to derive scene-specific pure material spectra. It consists of a well described starting spectral library (LUISA-A) and a tool to derive scene-based pure surface material spectra (LUISA-T). The concept is based on a three-stage approach: (1) Comparing hyperspectral image spectra with LUISA-A spectra to identify scene-specific pure materials, (2) extracting unknown pure spectra based on spatial and spectral metrics and (3) provides the framework to implement new surface material spectra into LUISA-A. The spectral comparison is based on several similarity measures, followed by an object- and spectral-based ruleset to optimize and categorize potentially new pure spectra.
The results show that the majority of pure surface materials could be identified using LUISA-A. Unknown spectra are composed of mixed pixels and real pure surface materials which could be distinguished by LUISA-T.
The Tasseled Cap Features, derived by the Tasseled Cap Transformation of the satellite spectral information, provide a way to consistently associate spectral information to biophysical characteristics of land surface features. Since currently there are no Tasseled Cap Coefficients available for RapidEye data, the goal of this study was to obtain Tasseled Cap Coefficients for the RapidEye sensors. As a result the Tasseled Cap Features Brightness, Greenness and Yellowness were derived. Brightness is a weighted sum of all bands and is aligned to the principal direction of soil brightness. Greenness contrasts the visual bands (including the Red Edge band) with the near infrared band, representing the spectral variation of vital vegetation. Yellowness contrast the Blue and Green bands with the Red, Red Edge and, to a lesser extent, NIR bands, and corresponds to the reflectance characteristics of dry, senescent crops. A transferability test of the Tasseled Cap Coefficients showed a successful application of the coefficients to other regions of the world, indicating a wider application potential.
Conference Committee Involvement (6)
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
15 September 2008 | Cardiff, Wales, United Kingdom
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology
17 September 2007 | Florence, Italy
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI
13 September 2006 | Stockholm, Sweden
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V
19 September 2005 | Bruges, Belgium
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV
14 September 2004 | Maspalomas, Canary Islands, Spain
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III
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