Development of a new approach for implementing the positional error model using Minkowski weighted average | |
| Author | Chanin Tinnachote |
| Call Number | AIT Diss. no.RS-06-4 |
| Subject(s) | Geographic information systems Minkowski geometry |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctoral of Engineering. |
| Publisher | Asian Institute of Technology |
| Abstract | Knowledge on positional uncertainty of a GIS data is vital for proper utilization of' those spatially related data and the information derived from them. Rigorous yet flexible model for positional error connected with geometric features in GIS is crucial to the modeling of error propagation and the appropriate handling of those errors. Quite a number of positional error models exist for point, line, and polygon features in the vector-based GIS. Being strictly derived from the 'point-based' approach, those models were found to possess limitation with respect to their applicability to general line and polygon features. This dissertation presents a new approach for implementing the existing positional pointbased error models in a more object-oriented manner. For this purpose, the classical concept of "Minkowski operations" which is normally utilized in Mathematical Morphology (MM) is employed. Multiple line and polygon datasets of the same area were considered as observations from which the weighted-average position of the features could be calculated using an object-based operation, called 'Minkowski weighted average'. In this study, a newly developed method for exercising Minkowski weighted average operation with vector objects is proposed. For line features, an algorithm for line feature segmentation and normalization was developed and tested. It is based on the `controlled' projection of the line's vertex points onto the compared line. As the result, the compared lines will be subdivided at all the projected positions into an equal number of segments. For polygon features, a new approach for polygon segmentation and normalization was also investigated and proposed. In the propose approach, the compared polygons are segmented into a network of irregular triangles and then the matching of triangles is performed based on the distance between their centroids. This is to get an equal number of convex subparts of the compared features which can be compared and averaged. The resulting weighted-average features can be used as the reference or "true" value in measuring error of each observation dataset. The proposed methods can also be used for measuring positional error of the observed line against the reference line. Several error measures such as mean error, minimum error, maximum error, Hausdorff distance, and RMSE can be extracted for point, line, and polygon objects simultaneously. A number of experimentations are carried out with both real and test datasets to demonstrate the validity and applicability of the proposed approach. To- realize those proposed methods newly developed in this study, a number of program tools have been prepared based on the PCArcView software. This indicates that there is quite a potential to incorporate the proposed object-based error handling methods as the built-in functions in commercial GIS packages especially when more efficient and robust algorithm for object normalization is made available. Results from the experimentations have proved that the proposed approach is very useful in extending the implementation of existing conceptual error model into practical work. In addition, the approach could also be employed to solve the problems in features matching and conflation. Nevertheless, future researches on the extensive algorithm for object normalization are still required. On-going researches on the development of a rigorous, generic, and object-oriented positional error model should also been carried out |
| Year | 2006 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Xiaoyong Chen;Kusanagi, Michiro |
| Examination Committee(s) | Hoang Le Tien;Kaew Nualchawee;Li, Zhilin |
| Scholarship Donor(s) | Government of Thailand (RTG) |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2006 |