Automatic single photo resection (SPR) remains one of the challenging problems in digital photogrammetry. Visibility and uniqueness of distinct control points in the input imagery limit robust automation of the space resection procedure. Recent advances in photogrammetry mandate adopting higher-level primitives, such as free-form control linear features, for replacing traditional control points. Linear features can be automatically extracted from the image space. On the other hand, object space control linear features can be obtained from an existing GIS layer containing 3D vector data such as road networks or from newly developed terrestrial mobile mapping systems (MMS). In this paper, two different approaches are presented for simultaneously determining the position and attitude of the imagery as well as the correspondence between image and object space linear features. These approaches are based on two representation schemes of the linear features. The first one represents the linear feature by a sequence of 2D and 3D points along the linear feature in the image and object space, respectively. The second scheme assumes that the feature is modelled by polylines (a sequence of straight-line segments). Neither approach requires one-to-one correspondence between image and object space primitives, which makes the suggested methodology robust against changes and/or discrepancies between the data-sets involved. This characteristic will be helpful in detecting and dealing with changes between object and image space linear features (due to temporal effects for example). The parameter estimation and matching follow an optimal sequential procedure that is developed and described within this paper, which depends on the sensitivity of the mathematical model relating corresponding primitives at various image regions to incremental changes in the exterior orientation parameters (EOP). Experiments are conducted to compare the algorithms' efficiency and the accuracy of the estimated EOP using both approaches. Experimental results using real data demonstrate the feasibility and robustness of both representation schemes as well as the methodologies developed. Moreover, different generalisation levels of the polylines representing the free-form linear features are compared.

In this study, sea level variation observed by a 1-Hz Global Positioning System (GPS) buoy system is verified by comparing with tide gauge records and is decomposed to reveal high-frequency signals that cannot be detected from 6-minute tide gauge records. Compared to tide gauges traditionally used to monitor sea level changes and affected by land motion, GPS buoys provide high-frequency geocentric measurements of sea level variations. Data from five GPS buoy campaigns near a tide gauge at Anping, Tainan, Taiwan, were processed using the Precise Point Positioning (PPP) technique with four different satellite orbit products from the International GNSS Service (IGS). The GPS buoy data were also processed by a differential GPS (DGPS) method that needs an additional GPS receiver as a reference station and the accuracy of the solution depends on the baseline length. The computation shows the average Root Mean Square Error (RMSE) difference of the GPS buoy using DGPS and tide gauge records is around 3 -5 cm. When using the aforementioned IGS orbit products for the buoy derived by PPP, its average RMSE differences are 5 -8 cm, 8 -13 cm, decimeter level, and decimeter-meter level, respectively, so the accuracy of the solution derived by PPP highly depends on the accuracy of IGS orbit products. Therefore, the result indicates that the accuracy of a GPS buoy using PPP has the potential to measure the sea surface variations to several cm. Finally, highfrequency sea level signals with periods of a few seconds to a day can be successfully detected in GPS buoy observations using the Ensemble Empirical Mode Decomposition (EMD) method and are identified as waves, meteotsunamis, and tides.

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