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Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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Classification of road network from airborne laser scanning data and from remote sensing images with high resolution
Kuchařová, Jana ; Potůčková, Markéta (advisor) ; Kupková, Lucie (referee)
Classification of road network from airborne laser scanning data and from remote sensing images with high resolution Abstract Object classification of land cover is currently one of the methods of remote Earth exploration. Road network classification only is unique because it is covered with anthropogenic material and has different characteristics than other elements of the landscape. This work deals with the possibility of using a combination of data from airborne laser scanning and high resolution optical data for detection of the road network in the specific area. The premise is that the use of two different types of data could provide better results, because airborne laser scanning data provide very precise information about the position and height of the point, while satellite data of very high resolution represent the real landscape. Searching for suitable features and classification rules for unambiguous determination of the road network is one of the objectives of the work. Segmentation parameters will also be important for object classification. Another objective is to verify the transferability of classification schemes into the other scene. The results should present a response on whether a procedure can be applied over a different location and also that the use of two types of data can bring...
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Classification of road network from airborne laser scanning data and from remote sensing images with high resolution
Kuchařová, Jana ; Potůčková, Markéta (advisor) ; Kupková, Lucie (referee)
Classification of road network from airborne laser scanning data and from remote sensing images with high resolution Abstract Object classification of land cover is currently one of the methods of remote Earth exploration. Road network classification only is unique because it is covered with anthropogenic material and has different characteristics than other elements of the landscape. This work deals with the possibility of using a combination of data from airborne laser scanning and high resolution optical data for detection of the road network in the specific area. The premise is that the use of two different types of data could provide better results, because airborne laser scanning data provide very precise information about the position and height of the point, while satellite data of very high resolution represent the real landscape. Searching for suitable features and classification rules for unambiguous determination of the road network is one of the objectives of the work. Segmentation parameters will also be important for object classification. Another objective is to verify the transferability of classification schemes into the other scene. The results should present a response on whether a procedure can be applied over a different location and also that the use of two types of data can bring...
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|
|
Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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