Kristian Kirk

Noise Characterization of Weighting Schemes for Combination of Multiple Exposures.

A method for capturing high intensity dynamic range scenes with a low dynamic range camera consists in taking a series of images with di erent exposure settings and combining these into a single high dynamic range image. The combined image values are found by weighted averaging of values from the di erently exposed images on a per-pixel basis. This paper reviews existing weighting schemes and considers their noise properties. Furthermore, a minimum-variance solution is introduced which exploits a camera noise model. Special emphasis is on the case when the camera is linear. A method is given for estimating the uncertainty of the combined image values. The results are validated experimentally.

Estimation of canopy structure parameters from multiangular measurements of scattering components

Structure parameters for characterization of vegetation canopies are often estimated from remote optical measurements. Existing methods include those based on measurements of gap fraction, spectral vegetation indices, or the inversion of spectral canopy reflectance models. This paper proposes a novel method based on inversion of multiangular measurements of the abundances of light scattering components, which may be estimated using spectral unmixing. An algorithm is described for predicting the abundances of various scattering components using Monte Carlo simulation with a Poisson canopy model and an ellipsoidal leaf angle distribution. The method was tested using simulated data from ray-traced images in a ground-based measurement scenario. Model fit surfaces were calculated for 20 different values of leaf area index (LAI) and mean leaf angle (MLA). The experiments generally showed good correspondances between observations and predictions, except for high values of LAI and low values of MLA. Future work should include experiments on real data and robust unmixing of scattering components.

The potential of using robotics in data acquisition from multiple sensors

This paper shows the potential of using robotics for data acquisition within full-scale field trials. Robotics ensured simultaneously measurements from several sensors from GPS targeted sampling points. This was demonstrated by supporting a project developing methods to measure gap fraction and canopy structure in cereals. The project required measurements from ordinary barley canopy areas using a high- dynamic-range RGB camera, and a multi-spectral Cropscan radiometer. Further, the RGB camera required images from 12 different angles relative to the rows. In fulfilling these demands an existing robotic platform at Research Center Bygholm, Denmark, was enhanced. A payload software system was developed ensuring a simple and efficient interface between the robotic platform and the multiple sensor systems. The software of the Cropscan Multispectral Radiometer System was also altered to support remote control by the payload software. The robotic system collected data from a full scale field at two occasions.