A comparison of methods for the retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR, and HRG multispectral satellite imagery

2010 ◽  
Vol 36 (4) ◽  
pp. 397-411 ◽  
Author(s):  
Barnaby Clark ◽  
Juha Suomalainen ◽  
Petri Pellikka
Keyword(s):  
Satellite Imagery ◽  
Surface Reflectance ◽  
Comparison Of Methods ◽  
Multispectral Satellite Imagery ◽  
Reflectance Factor

Detection by classification of buildings in multispectral satellite imagery

2016 ◽  
Author(s):  
Tomohiro Ishii ◽  
Edgar Simo-Serra ◽  
Satoshi Iizuka ◽  
Yoshihiko Mochizuki ◽  
Akihiro Sugimoto ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Multispectral Satellite Imagery

Linear spectral mixture modelling of arctic vegetation using ground spectroradiometry

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Anna Mikheeva ◽  
Anton Novichikhin ◽  
Olga Tutubalina
Keyword(s):  
Satellite Imagery ◽  
Ground Level ◽  
Spectral Unmixing ◽  
Atmospheric Effects ◽  
Arctic Vegetation ◽  
Mixture Modelling ◽  
New Information ◽  
Level Data ◽  
Multispectral Satellite Imagery ◽  
Spectral Mixture

ABSTRACTAn experimental linear mixture modelling using ground spectroradiometric measurements in the Kola Peninsula, Russia has been carried out to create a basis for mapping vegetation and non-vegetation components in the tundra-taiga ecotone using satellite imagery. We concentrated on the ground level experiment with the goal to use it further for the classification of multispectral satellite imagery through spectral unmixing. This experiment was performed on the most detailed level of remote sensing research which is free from atmospheric effects and easy to understand. We have measured typical ecotone components, including Cetraria nivalis, Betula tortuosa, Empetrum nigrum, Betula nana, Picea abies and rocks (nepheline syenite). The result of the experiment shows that the spectral mixture is indeed formed linearly but different components have different influence. Typical spectral thresholds for each component were found which are significant for vegetation mapping. Spectral unmixing of ground level data was performed and accuracy was estimated. The results add new information on typical spectral thresholds which can potentially be applied for multispectral satellite imagery when upscaling from high resolution to coarser resolution.

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