Volume I-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 353-358, 2012
https://doi.org/10.5194/isprsannals-I-7-353-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 353-358, 2012
https://doi.org/10.5194/isprsannals-I-7-353-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jul 2012

23 Jul 2012

HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM

E. Honkavaara1, J. Kaivosoja2, J. Mäkynen3, I. Pellikka4, L. Pesonen2, H. Saari3, H. Salo4, T. Hakala1, L. Marklelin1, and T. Rosnell1 E. Honkavaara et al.
  • 1Finnish Geodetic Institute, Geodeetinrinne 2, P.O. Box 15, FI-02431 Masala, Finland
  • 2MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland
  • 3VTT Photonic Devices and Measurement Solutions, P.O.Box 1000, FI-02044 VTT, Finland
  • 4Department of Mathematical Information Tech., University of Jyväskylä, P.O.Box 35, FI-40014, Jyväskylä, Finland

Keywords: Photogrammetry, Hyperspectral, Block, Point cloud, Geometry, Radiometry, Estimation, Agriculture

Abstract. The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, which is based on support vector regression machine. It was concluded that the central factors influencing on the accuracy of the estimation process were the quality of the image data, the quality of the image processing and digital surface model generation, and the performance of the regressor. In the wider perspective, our investigation showed that very low-weight, low-cost, hyperspectral, stereoscopic and spectrodirectional 3D UAV-remote sensing is now possible. This cutting edge technology is powerful and cost efficient in time-critical, repetitive and locally operated remote sensing applications.