'n Evaluering van Landsat MSS-data vir die bepaling van stedelike uitbreiding in die Verwoerdburg-Midrand omgewing, 1975-1988
- Authors: Pretorius, Theodor Gustav
- Date: 2014-06-05
- Subjects: Landsat satellites - Data processing , Remote sensing - Data processing
- Type: Thesis
- Identifier: uj:11424 , http://hdl.handle.net/10210/11062
- Description: M.Sc. (Geography) , The aim of this research is to determine if, by means of Landsat MSS digital data, urban land use classes can be identified and separated, and if changes in land use (urban sprawl) can be detected, over a period of time. Regional authorities function at inter-municipal scale. In order for these instittitions to perform these functions, they need to have access to standardized data (standardized in scale, time and interpretation) in order to obtain a global view of the total area under their authority. Remotely sensed digital data have the potential to fulfil these needs. A secondary objective will then also be to make an evaluation of the various applications of the results to the relevant authorities.
- Full Text:
- Authors: Pretorius, Theodor Gustav
- Date: 2014-06-05
- Subjects: Landsat satellites - Data processing , Remote sensing - Data processing
- Type: Thesis
- Identifier: uj:11424 , http://hdl.handle.net/10210/11062
- Description: M.Sc. (Geography) , The aim of this research is to determine if, by means of Landsat MSS digital data, urban land use classes can be identified and separated, and if changes in land use (urban sprawl) can be detected, over a period of time. Regional authorities function at inter-municipal scale. In order for these instittitions to perform these functions, they need to have access to standardized data (standardized in scale, time and interpretation) in order to obtain a global view of the total area under their authority. Remotely sensed digital data have the potential to fulfil these needs. A secondary objective will then also be to make an evaluation of the various applications of the results to the relevant authorities.
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Binary image features designed towards vision-based localization and environment mapping from micro aerial vehicle (MAV) captured images
- Authors: Cronje, Jaco
- Date: 2012-10-24
- Subjects: Binary image , Micro aerial vehicle captured images , Remote sensing - Data processing , Micro air vehicles , Mobile geographic information systems , Imaging systems
- Type: Thesis
- Identifier: http://ujcontent.uj.ac.za8080/10210/387081 , uj:10415 , http://hdl.handle.net/10210/7881
- Description: M.Phil. , This work proposes a fast local image feature detector and descriptor that is im- plementable on a GPU. The BFROST feature detector is the first published GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of the orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, the BFROST feature descriptor is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory usage. It is demonstrated that BFROST is usable in real-time applications such as vision-based localization and mapping of images captured from micro aerial platforms.
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- Authors: Cronje, Jaco
- Date: 2012-10-24
- Subjects: Binary image , Micro aerial vehicle captured images , Remote sensing - Data processing , Micro air vehicles , Mobile geographic information systems , Imaging systems
- Type: Thesis
- Identifier: http://ujcontent.uj.ac.za8080/10210/387081 , uj:10415 , http://hdl.handle.net/10210/7881
- Description: M.Phil. , This work proposes a fast local image feature detector and descriptor that is im- plementable on a GPU. The BFROST feature detector is the first published GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of the orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, the BFROST feature descriptor is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory usage. It is demonstrated that BFROST is usable in real-time applications such as vision-based localization and mapping of images captured from micro aerial platforms.
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The effect of data reduction on LiDAR-based DEMs
- Authors: Immelman, Jaco
- Date: 2012-11-02
- Subjects: Digital elevation models , Remote sensing - Data processing , Optical radar , Data reduction
- Type: Thesis
- Identifier: uj:7327 , http://hdl.handle.net/10210/8064
- Description: M.Sc. , Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data associated with LiDAR often require some form of data reduction to increase the data handling efficiency of these datasets, of which the latter could affect the feasibility of Digital Elevation Models (DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still represent the terrain adequately. This study investigated three different data reduction techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data reduction, in order to assess their effects on the accuracy, as well as the data handling efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 % were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30- m). Results show that, irrespective of terrain complexity, data points can be randomly reduced up to 25 % of the data points in the original dataset, with minimal effects on the remaining dataset. However, when these datasets are interpolated, data points can only be reduced to 50 % of the original data points, before showing large deviations from the original DEM. A reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to 4 metres before showing significant deviations. When combining point density reduction with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that reduce the processing time. When considering the effect on the accuracy, as well as the processing times of the data reduction techniques, results indicate that resolution reduction is the most effective data reduction technique. When reducing the grid resolution to 4 metres, data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data accuracy. Furthermore, this study investigated data reduction on a variety of terrain complexities and found that the reduction thresholds established by this study were applicable to both complex and non-complex terrain.
- Full Text:
- Authors: Immelman, Jaco
- Date: 2012-11-02
- Subjects: Digital elevation models , Remote sensing - Data processing , Optical radar , Data reduction
- Type: Thesis
- Identifier: uj:7327 , http://hdl.handle.net/10210/8064
- Description: M.Sc. , Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data associated with LiDAR often require some form of data reduction to increase the data handling efficiency of these datasets, of which the latter could affect the feasibility of Digital Elevation Models (DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still represent the terrain adequately. This study investigated three different data reduction techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data reduction, in order to assess their effects on the accuracy, as well as the data handling efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 % were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30- m). Results show that, irrespective of terrain complexity, data points can be randomly reduced up to 25 % of the data points in the original dataset, with minimal effects on the remaining dataset. However, when these datasets are interpolated, data points can only be reduced to 50 % of the original data points, before showing large deviations from the original DEM. A reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to 4 metres before showing significant deviations. When combining point density reduction with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that reduce the processing time. When considering the effect on the accuracy, as well as the processing times of the data reduction techniques, results indicate that resolution reduction is the most effective data reduction technique. When reducing the grid resolution to 4 metres, data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data accuracy. Furthermore, this study investigated data reduction on a variety of terrain complexities and found that the reduction thresholds established by this study were applicable to both complex and non-complex terrain.
- Full Text:
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