This diploma thesis solves the analysis of the influence of calibration and the method of strips alignment on the geometric accuracy of a point cloud acquired by UAV lidar scanning. The aim was to find out the influence of individual used methods, respectively various combinations. The effect of the design of the cross-flights has also been added. The evaluation was performed using standard deviations of the distances corresponding to the areas scanned in different point bands. Furthermore, verification was performed by comparing checkpoints. The results show that there is no dependence between the individual combinations. The only case was a larger displacement of the point cloud at the edge of the scanned strip in the case of cross-flights.

<p>Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSS) and inertial navigation systems (INS) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS unit and hyperspectral scanner to be evaluated. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This research presents three alternative calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, the thesis starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using Ground Control Points (GCPs) and one using tie points. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for ortho-rectification of six hyperspectral UAV datasets acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the Ground Sampling Distance (GSD) of the used scanner (namely, 3-5 cm when flying at 60 m).</p>

This thesis focuses on the comprehensive and thorough studies on the geospatial processing of airborne (full) waveform lidar data, including waveform modeling, direct georeferencing, and precise georeferencing with self-calibration.<div><br></div><div>Both parametric and nonparametric approaches of waveform decomposition are studied. The traditional parametric approach assumes that the returned waveforms follow a Gaussian mixture model where each component is a Gaussian. However, many real examples show that the waveform components can be neither Gaussian nor symmetric. To address the problem, this thesis proposes a nonparametric mixture model to represent lidar waveforms without any constraints on the shape of the waveform components. To decompose the waveforms, a fuzzy mean-shift algorithm is then developed. This approach has the following properties: 1) it does not assume that the waveforms follow any parametric or functional distributions; 2) the waveform decomposition is treated as a fuzzy data clustering problem and the number of components is determined during the process of decomposition; 3) neither peak selection nor noise floor filtering prior to the decomposition is needed; and 4) the range measurement is not affected by the process of noise filtering. In addition, the fuzzy mean-shift approach is about three times faster than the conventional expectationmaximization algorithm and tends to lead to fewer artifacts in the resultant digital elevation model. <br></div><div><br></div><div>This thesis also develops a framework and methodology of self-calibration that simultaneously determines the waveform geospatial position and boresight angles. Besides using the flight trajectory and plane attitude recorded by the onboard GPS receiver and inertial measurement unit, the framework makes use of the publically accessible digital elevation models as control over the study area. Compared to the conventional calibration and georeferencing method, the new development has minimum requirements on ground truth: no extra ground control, no planar objects, and no overlap flight strips are needed. Furthermore, it can also solve the problem of clock synchronization and boresight calibration simultaneously. Through a developed two-stage optimization strategy, the self-calibration approach can resolve both the time synchronization bias and boresight misalignment angles to achieve a stable and correct solution. As a result, a consistency of 0.8662 meter is achieved between the waveform derived digital elevation model and the reference one without systematic trend. Such experiments demonstrate the developed method is a necessary and more economic alternative to the conventional, high demanding georeferencing and calibration approach, especially when no or limited ground control is available.<br></div>

This dissertation revisits, develops, and documents methods that can be used to calibrate spaceborne microwave radiometers once in orbit. The on-orbit calibration methods discussed within this dissertation can provide accurate and early results by utilizing Calibration Attitude Maneuvers (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the Second Stokes (SS) analysis that can provide early and much needed insight on the performance of the instrument. This dissertation describes pre-existing and new methods of using DSC maneuvers as well as a simplified use of the SS procedure. Over TRMM's 17 years of operation it has provided invaluable data and has performed multiple CAMs over its lifetime. These maneuvers are analyzed to implement on-orbit calibration procedures that will be applied for future missions. In addition, this research focuses on the radiometric calibration of TMI that will be incorporated in the final processing (Archive/Legacy of the NASA TMI 1B11 brightness temperature data product). This is of importance since TMI's 17-year sensor data record must be vetted of all known calibration errors so to provide the final stable data for science users, specifically, climatological data records.