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Conference Paper

Automatic Shadow Detection in Hyperspectral VIS-NIR Images

Authors
/persons/resource/mbochow

Bochow,  Mathias
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/rogass

Rogaß,  Christian
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/kuester

Küster [Peisker],  Theres
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Heim,  B.
External Organizations;

Bartsch,  I.
External Organizations;

/persons/resource/segl

Segl,  Karl
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/charly

Kaufmann,  Hermann
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Bochow, M., Rogaß, C., Küster [Peisker], T., Heim, B., Bartsch, I., Segl, K., Kaufmann, H. (2011): Automatic Shadow Detection in Hyperspectral VIS-NIR Images, 7th EARSeL SIG Imaging Spectroscopy workshop (Edinburgh, Scotland 2011).


https://gfzpublic.gfz-potsdam.de/pubman/item/item_246301
Abstract
Depending on the landscape type high amounts of shadow can be present in remote sensing images. These areas are usually masked using shadow detection techniques and excluded from further analysis. Although significant research has been conducted on the detection of shadows there is still room for improvements. In this investigation we focus on the development of a new shadow detection algorithm capable to be automatically applied without user knowledge on any hyperspectral VIS-NIR image and thus can be implemented in automated pre-processing chains. The analysis is strictly focussed on the VIS-NIR part of the electromagnetic spectrum due to the growing number of VIS-NIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential shadow pixels and the removal of no-shadow pixels from this mask. In this context the separation between shadow and water is the most challenging task. By analysing different images containing inland and ocean water types we found the slope of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR range to be a diagnostic feature for water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. These aspects have been considered in the development of a knowledge-based classifier. First results indicate the great potential of the developed algorithm for urban, rural and coastal scenes of different sensor data (AISA, HyMap).