Abstract

Abstract. Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400–900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.

Highlights

  • Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology (Colomina and Molina, 2014)

  • In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400-900 nm (Honkavaara et al, 2014b)

  • The geometric processing workflow consists of the following three steps: 1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software (Wu 2011; Wu et al, 2013), 2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment, and 3) creating dense 3D point clouds and digital surface models (DSM) using Photogrammetric Surface Reconstruction from Imagery (SURE) software (Rothermel et al 2012) that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image

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Summary

Introduction

Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology (Colomina and Molina, 2014). UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. Typical flying altitudes are 10 m – 150 m and the areal extent ranges typically from few m2 to few km. UAVs can be used in various environmental remote sensing tasks, such as precision agriculture or water quality monitoring. They offer an interesting alternative to produce reflectance reference measurements for satellite sensor and image calibration and validation (cal/val). Several different UAV based spectrometric imaging techniques are already available, even for light-weight systems (Hruska et al, 2012; Zarco-Tejada et al, 2012; Buettner and Roeser 2014)

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