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Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration.
Intuitively, it is clear that neural processes and eye movements in reading are closely connected, but only few studies have investigated both signals simultaneously. Instead, the usual approach is to record them in separate experiments and to subsequently consolidate the results. However, studies using this approach have shown that it is feasible to coregister eye movements and EEG in natural reading and contributed greatly to the understanding of oculomotor processes in reading. The present thesis builds upon that work, assessing to what extent coregistration can be helpful for sentence processing research.
In the first study, we explore how well coregistration is suited to study subtle effects common to psycholinguistic experiments by investigating the effect of distance on dependency resolution. The results demonstrate that researchers must improve the signal-to-noise ratio to uncover more subdued effects in coregistration. In the second study, we compare oscillatory responses in different presentation modes. Using robust effects from world knowledge violations, we show that the generation and retrieval of memory traces may differ between natural reading and word-by-word presentation. In the third study, we bridge the gap between our knowledge of behavioral and neural responses to integration difficulties in reading by analyzing the EEG in the context of regressive saccades. We find the P600, a neural indicator of recovery processes, when readers make a regressive saccade in response to integration difficulties.
The results in the present thesis demonstrate that coregistration can be a useful tool for the study of sentence processing. However, they also show that it may not be suitable for some questions, especially if they involve subtle effects.