National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
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...
Possibilities of object-based classification for selected biotopes above tree-line in the Krkonoše Mts. National Park detection
Jakešová, Lucie ; Červená, Lucie (advisor) ; Potůčková, Markéta (referee)
Possibilities of object-based classification for selected biotopes above tree-line in the Krkonoše Mts. National Park detection Abstract The bachelor thesis is focused on the object-based classification of vegetation above the tree-line in the Krkonoše Mts. National Park using orthophoto with near infrared band and spatial resolution of 12.5 cm. Orthophoto was acquired in summer 2012. The classification legend was compiled by botanist of the national park. Software ENVI 5.1 was used for object-based classification using the field data. It provides two approaches to classification - Example-based and Rule-based. The overall accuracy of the best classification result was 75.97 % for 13 classes. Keywords: object-based classification, KRNAP, biotopes above tree-line, aerial optical scanner
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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