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Selected Publication:

Atzberger, C; Wess, M; Doneus, M; Verhoeven, G.
(2014): ARCTIS - A MATLAB (R) Toolbox for Archaeological Imaging Spectroscopy
REMOTE SENS-BASEL. 2014; 6(9): 8617-8638. FullText FullText_BOKU

Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data redundancy and the visualization of the large amount of data. This makes data mining approaches necessary, as well as efficient data visualization tools. Additional problems relate to data quality. Indeed, the upwelling electromagnetic radiation is recorded in small spectral bands that are only about ten nanometers wide. The signal received by the sensor is, thus quite low compared to sensor noise and possible atmospheric perturbations. The often small, instantaneous field of view (IFOV)-essential for archaeologically relevant imaging spectrometer datasets-further limits the useful signal stemming from the ground. The combination of both effects makes radiometric smoothing techniques mandatory. The present study details the functionality of a MATLAB (R)-based toolbox, called ARCTIS (ARChaeological Toolbox for Imaging Spectroscopy), for filtering, enhancing, analyzing, and visualizing imaging spectrometer datasets. The toolbox addresses the above-mentioned problems. Its Graphical User Interface (GUI) is designed to allow non-experts in remote sensing to extract a wealth of information from imaging spectroscopy for archaeological research. ARCTIS will be released under creative commons license, free of charge, via website (
Authors BOKU Wien:
Atzberger Clement
Wess Michael

Find related publications in this database (Keywords)
imaging spectroscopy
hyperspectral remote sensing
graphical user interface
vegetation mark
soil mark
visibility mark
aerial archaeology
data redundancy
data mining
red-edge inflection point
dimensionality reduction

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