Evaluation of a lightweigth UAS-prototype for hyperspectral imaging

Recent developments in the area of miniaturisation have boosted the development of a wide range of Unmanned Aerial Systems (UAS). In terms of hyperspectral imaging, no appropriate sensors are available for light weight UAS. Available sensors are in general too heavy, produce an overload on data, and are mainly 1D line sensors. Given the attitude instability of a light weight UAV in combination with inferior miniaturized attitude sensors, accurate image reconstruction becomes an exceptional challenge for such line sensors mounted on a UAS. The first trials with a novel UAS equipped with an hyperspectral frame imager are presented. The imager was integrated and operated on a six-rotor rotorcraft type UAV of VITO, the Flemisch Institute for Technological Research. This system provides an answer to the above mentioned issues: two dimensional imaging, low weight and with user definable (application specific) spectral sensitivity. The hyperspectral imager was developed by VTT Technical Research Centre of Finland and is based on the Piezo actuated FabryPerot Interferometer to enable recording of 2D spatial images at the selected wavelength bands simultaneously and to reduce the size of the hyperspectral spectrometer to be compatible with light weight UAV platforms. The prototype is capable of recording 2D images within the range of 400 – 1100 nm at a spectral resolution of 5 – 10 nm @ FWHM. The full sensor system (sensor, storage, battery and triggering) weighted less than 500 grams. The results and key issues for improvement for different aspects of the UAS will be presented during the symposium.

[1]  A. Moccia,et al.  An Integrated Electro-Optical Payload System for Forest Fires Monitoring from Airborne Platform , 2007, 2007 IEEE Aerospace Conference.

[2]  Won Suk Lee,et al.  Remotely-Piloted Helicopter Citrus Yield Map Estimation , 2006 .

[3]  Yongcan Cao,et al.  Band-reconfigurable Multi-UAV-based Cooperative Remote Sensing for Real-time Water Management and Distributed Irrigation Control , 2008 .

[4]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[5]  B. Merkel,et al.  Low‐cost aerial photography for high‐resolution mapping of hydrothermal areas in Yellowstone National Park , 2008 .

[6]  John R. Miller,et al.  Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[7]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[8]  Stephan Nebiker,et al.  A LIGHT-WEIGHT MULTISPECTRAL SENSOR FOR MICRO UAV – OPPORTUNITIES FOR VERY HIGH RESOLUTION AIRBORNE REMOTE SENSING , 2008 .

[9]  Stanley R. Herwitz,et al.  Collection of Ultra High Spatial and Spectral Resolution Image Data over California Vineyards with a Small UAV , 2003 .

[10]  P. Zarco-Tejada,et al.  Modelling PRI for water stress detection using radiative transfer models , 2009 .

[11]  Görres Grenzdörffer,et al.  THE PHOTOGRAMMETRIC POTENTIAL OF LOW-COST UAVs IN FORESTRY AND AGRICULTURE , 2008 .

[12]  H. Eisenbeiss,et al.  Combining photogrammetry and laser scanning for the recording and modelling of the Late Intermediate Period site of Pinchango Alto, Palpa, Peru , 2007 .

[13]  Jeffrey G. White,et al.  Aerial Color Infrared Photography to Optimize In‐Season Nitrogen Fertilizer Recommendations in Winter Wheat , 2007 .

[14]  Heikki Saari,et al.  Novel miniaturized hyperspectral sensor for UAV and space applications , 2009, Remote Sensing.